From dd6fd0749afe40085cfe9817c98291857d192d40 Mon Sep 17 00:00:00 2001 From: igerber Date: Sun, 12 Jul 2026 15:22:40 -0400 Subject: [PATCH 1/3] feat: add ChangesInChanges (CiC) + QDiD distributional DiD estimators Athey & Imbens (2006) nonlinear DiD for the 2x2 design with continuous outcomes: ChangesInChanges (alias CiC) recovers the treated group's full counterfactual outcome distribution and quantile treatment effects; QDiD is the paper's additive quantile-DiD comparison estimator. - Point estimation matches R qte 1.3.1 exactly: CiC via an exact port of R quantile.default type-1 arithmetic incl. fuzz (bit-exact golden parity); QDiD via qte's type-7 additive form (REGISTRY Note documents the finite-sample deviation from the paper's k^QDID transformation) - Bootstrap-only inference per qte's schemes (panel unit-block / RCS pooled resample; seedable; n_bootstrap=200; replicate-SD SEs, normal CIs, sup-t uniform bands at qte's fixed 95% level) - Diagnostics: eq.-17 interior-range guard (NaN inference outside), support-violation / heavy-ties / QDiD-non-monotonicity warnings; joint-NaN inference contract via safe_inference(_batch) - Golden fixtures: benchmarks/R/generate_qte_golden.R (qte pinned 1.3.1 in requirements.R) -> benchmarks/data/qte_golden.json; parity tests cover 4 scenarios x {CiC,QDiD} x {panel,RCS} at atol=1e-10, type-1 micro-fixtures at atol=0, seeded-R SE block statistically - Docs: REGISTRY entry + cross-reference rows, api rst, references, llms(-full).txt, README/choosing/r_comparison/practitioner catalogs, estimator count 20 -> 22 (incl. paper.md + new Athey2006 bib entry), TODO deferral rows (covariates/bounds/analytical-SE/staggered/ATC + tutorial and practitioner-handler follow-ups) Deferred and documented per the merged PR-A review stack (#678): covariates, discrete-outcome bounds, analytical SEs, staggered designs, treatment-on-controls. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_011hismLBFcUbUzvDRq8ruWb --- CHANGELOG.md | 18 + README.md | 1 + TODO.md | 10 + benchmarks/R/generate_qte_golden.R | 213 +++++ benchmarks/R/requirements.R | 5 +- benchmarks/data/qte_golden.json | 1 + diff_diff/__init__.py | 14 + diff_diff/changes_in_changes.py | 867 +++++++++++++++++++ diff_diff/changes_in_changes_results.py | 231 +++++ diff_diff/guides/llms-full.txt | 70 +- diff_diff/guides/llms.txt | 4 +- docs/api/changes_in_changes.rst | 182 ++++ docs/api/index.rst | 4 + docs/choosing_estimator.rst | 14 + docs/doc-deps.yaml | 31 + docs/methodology/REGISTRY.md | 77 +- docs/practitioner_decision_tree.rst | 4 +- docs/r_comparison.rst | 2 + docs/references.rst | 7 + paper.bib | 11 + paper.md | 11 +- tests/test_aliases.py | 2 + tests/test_changes_in_changes.py | 493 +++++++++++ tests/test_changes_in_changes_parity.py | 201 +++++ tests/test_methodology_changes_in_changes.py | 293 +++++++ 25 files changed, 2755 insertions(+), 11 deletions(-) create mode 100644 benchmarks/R/generate_qte_golden.R create mode 100644 benchmarks/data/qte_golden.json create mode 100644 diff_diff/changes_in_changes.py create mode 100644 diff_diff/changes_in_changes_results.py create mode 100644 docs/api/changes_in_changes.rst create mode 100644 tests/test_changes_in_changes.py create mode 100644 tests/test_changes_in_changes_parity.py create mode 100644 tests/test_methodology_changes_in_changes.py diff --git a/CHANGELOG.md b/CHANGELOG.md index a8e58b15b..b9669d72e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -30,6 +30,24 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 main — no `ready-for-ci` label needed; the aggregate `Lint Gate` job is the single required-check name. Plus `.git-blame-ignore-revs` covering the 2026-07 bulk normalization commits, and lint sections in CONTRIBUTING.md / CLAUDE.md. +- **ChangesInChanges (Athey & Imbens 2006) + QDiD estimators.** Distributional + DiD for the canonical 2x2 design with continuous outcomes: `ChangesInChanges` + (alias `CiC`) recovers the treated group's full counterfactual outcome + distribution and quantile treatment effects via the CDF transformation + `F_10(F_00^{-1}(F_01(y)))`, invariant to monotone outcome rescaling; `QDiD` + is the paper's additive quantile-DiD comparison estimator. Point estimation + matches R `qte` 1.3.1 exactly (CiC via an exact port of R's type-1 quantile + arithmetic incl. its fuzz handling - bit-exact golden parity; QDiD via qte's + type-7 form, documented in REGISTRY.md as a finite-sample deviation from the + paper's transformation, population-equivalent). Bootstrap-only inference + (panel unit-block or pooled repeated-cross-section resampling per qte's + schemes; seedable, `n_bootstrap=200` default; replicate-SD SEs, normal + intervals, sup-t uniform bands at qte's fixed 95% level). Diagnostics: + interior-range guard (eq. 17) with NaN inference outside, support-violation / + heavy-ties / QDiD-non-monotonicity warnings. Deferred and documented: + covariates, discrete-outcome bounds, analytical SEs, staggered designs. + Golden fixtures: `benchmarks/R/generate_qte_golden.R` (qte pinned 1.3.1) -> + `benchmarks/data/qte_golden.json`. ### Changed - **Internal: repo-wide lint normalization + pinned tooling.** black/ruff/mypy are now diff --git a/README.md b/README.md index c5e46f4a8..d442de46d 100644 --- a/README.md +++ b/README.md @@ -118,6 +118,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`. - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html) - Wooldridge (2023, 2025) ETWFE: saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias `ETWFE`. - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html) - Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting), variance- or equally-weighted ATT, for absorbing or non-absorbing (reversible) treatment +- [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html) - Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: full counterfactual distribution and quantile treatment effects via CDF transformation, plus the QDiD comparison estimator; bootstrap inference; R qte parity. Alias `CiC` - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html) - Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings ## Diagnostics & Sensitivity diff --git a/TODO.md b/TODO.md index 5fa50d47a..a3adba541 100644 --- a/TODO.md +++ b/TODO.md @@ -45,6 +45,11 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m ### Testing / docs +| Issue | Location | Origin | Effort | Priority | +|-------|----------|--------|--------|----------| +| ChangesInChanges/QDiD tutorial notebook (2x2 distributional walkthrough: QTE grid, interior range, uniform bands, CiC-vs-QDiD comparison) - deferred from the implementation PR as a documented decision. | `docs/tutorials/` | CiC PR-B | Mid | Low | +| `practitioner_next_steps()` dedicated handler for `ChangesInChangesResults` (currently falls back to `_handle_generic`, which is safe; a dedicated handler is the established full-integration step, cf. HAD Phase 5). | `diff_diff/practitioner.py` | CiC PR-B | Quick | Low | + --- ## Deferred / Documented @@ -105,6 +110,11 @@ Doable in principle, but no current caller and/or explicitly out of paper scope. | Issue | Location | PR | Priority | |-------|----------|----|----------| +| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | - | Low | +| ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | - | Low | +| ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | - | Low | +| Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | - | Low | +| ChangesInChanges treatment-on-the-controls (Athey-Imbens Theorem 3.2: group-label exchange + negation; no qte equivalent to anchor parity). | `diff_diff/changes_in_changes.py` | - | Low | | Rust-backend CR2 Bell-McCaffrey port (`return_dof` in the Rust vcov dispatch + CR2 algebra) — **premise re-scoped 2026-07-09**: the scores-based DOF + low-rank factored `A_g` changes made the NumPy CR2-BM path BLAS-bound (`O(n_g k²)` per cluster; 4.1s→38ms at n=100k/k=40), so a Rust port buys ~nothing and adds a parity surface. Revisit only if profiling shows CR2-BM hot again. | `rust/src/linalg.rs` | — | Low | | Clustered-CR1 inference df **default flip to `"cluster"` (G−1) at v4** — the opt-in `df_convention=` knob landed 2026-07 (DiD/TWFE/MPD + LinearRegression; REGISTRY §TwoWayFixedEffects deviation note); the remaining work is the major-version default change (moves every clustered p-value/CI) + migration note + flipping `TestDfConvention`/`test_moderate_t_pins_residual_df_convention` expectations. Also evaluate extending the knob to standalone estimators with CR1-t inference at that time. | `diff_diff/linalg.py::LinearRegression`, `diff_diff/estimators.py`, `diff_diff/twfe.py` | — | Medium | | CallawaySantAnna **unbalanced-panel R parity — LANDED** via `allow_unbalanced_panel=True` (matches R `did::att_gt(allow_unbalanced_panel=TRUE)` / `DRDID::reg_did_rc`: ATT bit-exact on cells AND dynamic aggregation via fixed unit-cohort-mass `pg` + a per-unit WIF; SE up to the documented CR1 `sqrt(G/(G-1))` factor). The earlier "weighting" framing was a mis-diagnosis — on unbalanced panels the dominant divergence from R is the *estimator* (within-cell differencing vs RC-on-pooled-obs), not only the weighting; both are resolved by the flag. The DEFAULT path keeps within-cell differencing as a documented design choice and now emits a `UserWarning` on unbalanced input (no-silent-failures). **Remaining deferred:** `survey_design=` × `allow_unbalanced_panel=` (per-obs vs per-unit weight resolution — currently fail-closed `NotImplementedError`); and covariate / ipw / dr × the flag R-parity verification (the RC path supports them; the committed golden covers `reg` no-cov). | `staggered.py`, `staggered_aggregation.py` | SE-audit D3 | Low | diff --git a/benchmarks/R/generate_qte_golden.R b/benchmarks/R/generate_qte_golden.R new file mode 100644 index 000000000..08ac2cab9 --- /dev/null +++ b/benchmarks/R/generate_qte_golden.R @@ -0,0 +1,213 @@ +#!/usr/bin/env Rscript +# Golden fixtures for the diff-diff ChangesInChanges / QDiD estimators vs qte 1.3.1. +# +# Regenerate with: +# Rscript benchmarks/R/generate_qte_golden.R +# +# Output: benchmarks/data/qte_golden.json, consumed by +# tests/test_changes_in_changes_parity.py (which pytest.skips when absent). +# +# Version contract: qte is pinned to 1.3.1 here, in benchmarks/R/requirements.R +# (pinned_versions), and in the parity test's test_metadata_versions_match. +# Bump all three in lockstep when re-anchoring. +# +# Point fixtures are generated with se=FALSE so they are fully deterministic +# (qte's bootstrap is not seedable through its public API). The SE block runs +# seeded (set.seed before each call) with iters=999; Python compares those SEs +# statistically, not bit-exactly. + +suppressMessages({ + library(qte) + library(jsonlite) +}) + +stopifnot(packageVersion("qte") == "1.3.1") + +probs <- seq(0.05, 0.95, 0.05) + +# --------------------------------------------------------------------------- +# Synthetic DGPs (long format: id, period 0/1, treat 0/1 group indicator, y) +# --------------------------------------------------------------------------- + +make_normal_2x2 <- function(n_treated, n_control, seed) { + # Additive time shift + heterogeneous treatment effect monotone in the unit + # unobservable; full support overlap so the whole grid is interior. + set.seed(seed) + n <- n_treated + n_control + treat <- c(rep(1L, n_treated), rep(0L, n_control)) + u <- rnorm(n, mean = 0, sd = 1) + y_pre <- u + rnorm(n, sd = 0.3) + effect <- 0.8 + 0.4 * pnorm(u) + y_post <- u + 0.5 + rnorm(n, sd = 0.3) + treat * effect + data.frame( + id = rep(seq_len(n), times = 2), + period = rep(c(0L, 1L), each = n), + treat = rep(treat, times = 2), + y = c(y_pre, y_post) + ) +} + +make_lognormal_2x2 <- function(n_treated, n_control, seed) { + # Skewed outcome with a scale-type change over time: CiC and QDiD are + # numerically far apart, exercising the inverse-CDF composition away from + # the additive special case. + set.seed(seed) + n <- n_treated + n_control + treat <- c(rep(1L, n_treated), rep(0L, n_control)) + u <- rnorm(n, mean = 0, sd = 0.6) + y_pre <- exp(u + rnorm(n, sd = 0.2)) + y_post <- exp(u * 1.3 + 0.3 + rnorm(n, sd = 0.2)) + treat * (0.5 + 0.5 * exp(u / 2)) + data.frame( + id = rep(seq_len(n), times = 2), + period = rep(c(0L, 1L), each = n), + treat = rep(treat, times = 2), + y = c(y_pre, y_post) + ) +} + +dgps <- list( + normal_2x2_n500 = make_normal_2x2(250, 250, seed = 20260712), + lognormal_2x2_n300 = make_lognormal_2x2(150, 150, seed = 20260713), + # Small unbalanced-cell design stressing near-integer n*p type-1 boundaries. + smalln_2x2_n60 = make_normal_2x2(35, 25, seed = 20260714) +) + +# lalonde (qte-shipped): 1975/1978 two-period panel; re78 zeros give heavy ties. +data(lalonde, package = "qte") +lal <- lalonde.psid.panel[lalonde.psid.panel$year %in% c(1975, 1978), ] +dgps$lalonde_psid <- data.frame( + id = lal$id, + period = ifelse(lal$year == 1978, 1L, 0L), + treat = as.integer(lal$treat), + y = as.numeric(lal$re) +) + +# --------------------------------------------------------------------------- +# Point fixtures: {CiC, QDiD} x {panel, repeated cross-section}, se = FALSE +# --------------------------------------------------------------------------- + +run_point <- function(df, method, panel) { + fn <- if (method == "cic") CiC else QDiD + res <- fn( + formla = y ~ treat, + t = 1, tmin1 = 0, tname = "period", + data = df, + panel = panel, + idname = if (panel) "id" else NULL, + se = FALSE, + probs = probs + ) + list(ate = res$ate, qte = as.numeric(res$qte)) +} + +scenarios <- list() +for (dgp_name in names(dgps)) { + df <- dgps[[dgp_name]] + entry <- list( + data = list( + id = df$id, + period = df$period, + treat = df$treat, + y = df$y + ), + results = list() + ) + for (method in c("cic", "qdid")) { + for (panel in c(TRUE, FALSE)) { + key <- sprintf("%s_%s", method, if (panel) "panel" else "rcs") + entry$results[[key]] <- run_point(df, method, panel) + message(sprintf( + "%s / %s: ate = %.10g", dgp_name, key, entry$results[[key]]$ate + )) + } + } + scenarios[[dgp_name]] <- entry +} + +# --------------------------------------------------------------------------- +# SE block (statistical parity): normal_2x2_n500 only, seeded, iters = 999 +# --------------------------------------------------------------------------- + +run_se <- function(df, method, panel, seed) { + fn <- if (method == "cic") CiC else QDiD + set.seed(seed) + res <- fn( + formla = y ~ treat, + t = 1, tmin1 = 0, tname = "period", + data = df, + panel = panel, + idname = if (panel) "id" else NULL, + se = TRUE, iters = 999, + probs = probs + ) + list( + ate = res$ate, qte = as.numeric(res$qte), + ate_se = res$ate.se, qte_se = as.numeric(res$qte.se), + sup_t_crit = as.numeric(res$c) + ) +} + +se_block <- list( + cic_panel = run_se(dgps$normal_2x2_n500, "cic", TRUE, seed = 42), + cic_rcs = run_se(dgps$normal_2x2_n500, "cic", FALSE, seed = 42), + qdid_panel = run_se(dgps$normal_2x2_n500, "qdid", TRUE, seed = 42) +) +message("SE block done.") + +# --------------------------------------------------------------------------- +# Micro-fixtures: raw R type-1 / type-7 quantile outputs on adversarial probs +# --------------------------------------------------------------------------- + +micro_case <- function(x, p) { + list( + x = x, + probs = p, + type1 = as.numeric(quantile(x, probs = p, type = 1, names = FALSE)), + type7 = as.numeric(quantile(x, probs = p, names = FALSE)) + ) +} + +set.seed(20260715) +x7 <- sort(rnorm(7)) +x20 <- sort(rnorm(20)) +x60 <- sort(rlnorm(60)) +x101 <- sort(rnorm(101)) +# ECDF-composed probabilities (the parity-critical case): ranks k/n00 from one +# sample evaluated in another - float products can land an ulp off an integer. +p_comp20 <- ecdf(x7)(x20) +p_comp60 <- ecdf(x20)(x60) +grid_edges <- c(0, 1e-12, 1 / 7, 2 / 7, 0.25, 1 / 3, 0.5, 19 / 20, 0.95, 0.999, 1) +p_near_int101 <- c((0:101) / 101, (1:100) / 101 + 1e-16, (1:100) / 101 - 1e-16) + +micro <- list( + n7_grid = micro_case(x7, grid_edges), + n20_composed = micro_case(x20, p_comp20), + n60_composed = micro_case(x60, p_comp60), + n101_near_integer = micro_case(x101, pmin(pmax(p_near_int101, 0), 1)) +) + +# --------------------------------------------------------------------------- +# Write JSON +# --------------------------------------------------------------------------- + +out <- list( + metadata = list( + description = paste( + "Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte.", + "Point fixtures use se=FALSE (deterministic); the se_block is seeded", + "(set.seed before each call, iters=999) and compared statistically." + ), + qte_version = as.character(packageVersion("qte")), + r_version = as.character(getRversion()), + probs = probs, + point_atol = 1e-10, + n_scenarios = length(scenarios) + ), + scenarios = scenarios, + se_block = se_block, + quantile_cases = micro +) + +out_path <- file.path("benchmarks", "data", "qte_golden.json") +write_json(out, path = out_path, digits = 17, auto_unbox = TRUE, null = "null") +message(sprintf("Wrote %s (%.1f KB)", out_path, file.size(out_path) / 1024)) diff --git a/benchmarks/R/requirements.R b/benchmarks/R/requirements.R index a5499687f..c20628b10 100644 --- a/benchmarks/R/requirements.R +++ b/benchmarks/R/requirements.R @@ -18,6 +18,7 @@ required_packages <- c( "YatchewTest", # Yatchew (1997) linearity test (HAD yatchew R-parity) "nprobust", # Calonico-Cattaneo-Farrell local-linear (DIDHAD dependency) "Synth", # Abadie-Diamond-Hainmueller (2010) synthetic control (SyntheticControl R-parity; ships data(basque)) + "qte", # Callaway qte package (Athey-Imbens CiC + QDiD R-parity; ships data(lalonde)) # Utilities "jsonlite", # JSON output for Python interop @@ -71,7 +72,9 @@ install_pinned_version <- function(pkg, version) { pinned_versions <- list( DIDHAD = "2.0.0", YatchewTest = "1.1.1", - nprobust = "0.5.0" + nprobust = "0.5.0", + # CiC/QDiD R-parity (generate_qte_golden.R + tests/test_changes_in_changes_parity.py) + qte = "1.3.1" ) install_github_if_missing <- function(pkg, repo) { diff --git a/benchmarks/data/qte_golden.json b/benchmarks/data/qte_golden.json new file mode 100644 index 000000000..89e0f3e31 --- /dev/null +++ b/benchmarks/data/qte_golden.json @@ -0,0 +1 @@ +{"metadata":{"description":"Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte. Point fixtures use se=FALSE (deterministic); the se_block is seeded (set.seed before each call, iters=999) and compared 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diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 68e9de64b..124dd98bb 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -47,6 +47,14 @@ chaisemartin_dhaultfoeuille, twowayfeweights, ) +from diff_diff.changes_in_changes import ( + ChangesInChanges, + QDiD, +) +from diff_diff.changes_in_changes_results import ( + ChangesInChangesResults, + QDiDResults, +) from diff_diff.chaisemartin_dhaultfoeuille_results import ( ChaisemartinDHaultfoeuilleResults, DCDHBootstrapResults, @@ -299,6 +307,7 @@ ETWFE = WooldridgeDiD DCDH = ChaisemartinDHaultfoeuille HAD = HeterogeneousAdoptionDiD +CiC = ChangesInChanges __version__ = "3.7.0" __all__ = [ @@ -309,6 +318,8 @@ "SyntheticDiD", "CallawaySantAnna", "ChaisemartinDHaultfoeuille", + "ChangesInChanges", + "QDiD", "ContinuousDiD", "SunAbraham", "ImputationDiD", @@ -326,6 +337,7 @@ "CS", "CDiD", "DCDH", + "CiC", "SA", "BJS", "Gardner", @@ -346,6 +358,8 @@ "CallawaySantAnnaResults", "CSBootstrapResults", "GroupTimeEffect", + "ChangesInChangesResults", + "QDiDResults", "ContinuousDiDResults", "DoseResponseCurve", "SunAbrahamResults", diff --git a/diff_diff/changes_in_changes.py b/diff_diff/changes_in_changes.py new file mode 100644 index 000000000..666eb9d2f --- /dev/null +++ b/diff_diff/changes_in_changes.py @@ -0,0 +1,867 @@ +"""Changes-in-Changes (CiC) and Quantile Difference-in-Differences (QDiD) estimators. + +Implements the nonlinear difference-in-differences estimators of Athey & Imbens +(2006), "Identification and Inference in Nonlinear Difference-in-Differences +Models", Econometrica 74(2), 431-497, for the canonical 2x2 design (two groups, +two periods) with continuous outcomes: + +- :class:`ChangesInChanges` (alias ``CiC``): the changes-in-changes estimator. + The counterfactual second-period outcome distribution of the treated group is + ``F_{Y^N,11}(y) = F_10(F_00^{-1}(F_01(y)))`` (Theorem 3.1, eq. 9); the ATT is + the plug-in eq. (36) and quantile treatment effects follow eqs. (17)-(18). +- :class:`QDiD`: the quantile DiD comparison estimator (Section 3.3). The + authors recommend CiC over QDiD (p. 447): QDiD's justifying model is not + invariant to monotone rescaling of the outcome and places testable + restrictions on the data. + +Numerical conventions match the R ``qte`` package (v1.3.1, Callaway), the +project's parity target, exactly: + +- CiC uses R type-1 quantiles (the paper's eq. (35)/(A.1) inf-based + ceiling-order-statistic inverse) throughout. +- QDiD uses the additive quantile-DiD form with R type-7 (linear-interpolation) + quantiles, matching ``qte::QDiD()``. This is population-equivalent to the + paper's ``k^QDID`` transformation but a different finite-sample estimator + (see the labeled Note in docs/methodology/REGISTRY.md). +- Inference is bootstrap-only (the paper's analytical influence-function + variance is deferred): panel mode resamples units (both periods travel + together); repeated cross-section mode draws a single pooled row resample. + SEs are SDs over replicates with symmetric normal-approximation intervals. + +Scope (v1, per docs/methodology/papers/athey-imbens-2006-review.md): 2x2 +design, continuous outcomes, no covariates. Deferred and documented in the +methodology registry: covariates (Melly-Santangelo 2015), discrete-outcome +bounds (Section 4), analytical standard errors (Theorems 5.1-5.7), multiple +groups/periods (Section 6), and treatment-on-the-controls (Theorem 3.2). +""" + +import warnings +from typing import Any, Callable, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +from scipy import stats + +from diff_diff.bootstrap_utils import warn_bootstrap_failure_rate +from diff_diff.changes_in_changes_results import ChangesInChangesResults +from diff_diff.utils import safe_inference, safe_inference_batch, validate_binary + +# Default quantile grid: matches qte's ``probs = seq(0.05, 0.95, 0.05)`` (19 points). +_DEFAULT_QUANTILES = np.arange(0.05, 0.96, 0.05) + +# Duplicate-share threshold above which the discrete-outcome warning fires. Library +# choice: the paper's continuous machinery (Assumption 5.1(iii)) has no finite-sample +# ties rule, and applying it to discrete data silently returns one endpoint of the +# Section 4 bounds rather than a point estimate. +_TIE_SHARE_WARN = 0.10 + +# Minimum share of finite bootstrap replicate rows required to report SEs +# (bootstrap_utils convention). +_MIN_VALID_REPLICATE_SHARE = 0.5 + +_CELL_LABELS = { + "y00": "control pre-period (treatment=0, time=0)", + "y01": "control post-period (treatment=0, time=1)", + "y10": "treated pre-period (treatment=1, time=0)", + "y11": "treated post-period (treatment=1, time=1)", +} + + +# ============================================================================= +# Numeric core +# ============================================================================= + + +def _build_cells(y: np.ndarray, g: np.ndarray, t: np.ndarray) -> Optional[Dict[str, np.ndarray]]: + """Split outcomes into the four sorted (group, period) cells. + + Returns ``None`` if any cell is empty (bootstrap replicates use this to + signal a failed draw; ``fit`` raises instead via :func:`_split_cells`). + """ + cells = {} + for key, (gv, tv) in {"y00": (0, 0), "y01": (0, 1), "y10": (1, 0), "y11": (1, 1)}.items(): + cell = y[(g == gv) & (t == tv)] + if cell.size == 0: + return None + cells[key] = np.sort(cell) + return cells + + +def _split_cells(y: np.ndarray, g: np.ndarray, t: np.ndarray) -> Dict[str, np.ndarray]: + """Like :func:`_build_cells` but raises on empty cells (Assumption 5.1(ii)).""" + for key, (gv, tv) in {"y00": (0, 0), "y01": (0, 1), "y10": (1, 0), "y11": (1, 1)}.items(): + if not np.any((g == gv) & (t == tv)): + raise ValueError( + f"Empty (group, period) cell: no observations in the {_CELL_LABELS[key]} cell. " + "All four 2x2 cells must be non-empty (Athey-Imbens Assumption 5.1(ii))." + ) + cells = _build_cells(y, g, t) + assert cells is not None + return cells + + +def _ecdf(sorted_sample: np.ndarray, x: np.ndarray) -> np.ndarray: + """Empirical CDF of ``sorted_sample`` evaluated at ``x`` (eq. 34, ``<=`` semantics). + + Values below the sample minimum map to 0.0; at or above the maximum to 1.0. + """ + n = sorted_sample.shape[0] + return np.searchsorted(sorted_sample, x, side="right") / n + + +def _quantile_type1(sorted_sample: np.ndarray, probs: np.ndarray) -> np.ndarray: + """R ``quantile(x, probs, type=1)`` - exact port including R's fuzz arithmetic. + + This is the paper's eq. (35)/(A.1) inf-based inverse: the ceiling order + statistic ``x_(ceil(n*p))`` with ``F^{-1}(0)`` = sample minimum. R computes + ``j = floor(n*p * (1 + fuzz))`` with ``fuzz = 4 * .Machine$double.eps`` and + ``h = (n*p > j)``, returning the 1-based order statistic ``x[j + h]``. The + fuzz matters because ECDF-composed probabilities like ``k/n00`` can land one + ulp above an integer after multiplication by ``n01``; a naive ceil picks a + different order statistic than R there. Do not replace this with + ``np.quantile(method="inverted_cdf")`` - its arithmetic differs from R's. + """ + n = sorted_sample.shape[0] + p = np.clip(np.asarray(probs, dtype=float), 0.0, 1.0) + fuzz = 4.0 * np.finfo(float).eps + nppm = n * p + j = np.floor(nppm * (1.0 + fuzz)).astype(np.int64) + h = (nppm > j).astype(np.int64) + idx = np.clip(j + h, 1, n) + return sorted_sample[idx - 1] + + +def _quantile_type7(sorted_sample: np.ndarray, probs: np.ndarray) -> np.ndarray: + """R ``quantile(x, probs)`` default type-7 == numpy's default ``linear`` method.""" + p = np.clip(np.asarray(probs, dtype=float), 0.0, 1.0) + return np.quantile(sorted_sample, p, method="linear") + + +def _cic_point( + cells: Dict[str, np.ndarray], quantiles: np.ndarray +) -> Tuple[float, np.ndarray, np.ndarray]: + """CiC ATT (eq. 36) and quantile effects (eq. 18), qte::CiC() arithmetic. + + Counterfactual draws: each treated pre-period outcome is ranked in the + control pre-period distribution and pushed through the type-1 quantile of + the control post-period distribution (``F_01^{-1}(F_00(y))``, eq. 15/36). + """ + ranks = _ecdf(cells["y00"], cells["y10"]) + cf = _quantile_type1(cells["y01"], ranks) + att = float(np.mean(cells["y11"]) - np.mean(cf)) + cf_sorted = np.sort(cf) + qte = _quantile_type1(cells["y11"], quantiles) - _quantile_type1(cf_sorted, quantiles) + return att, qte, cf_sorted + + +def _qdid_point(cells: Dict[str, np.ndarray], quantiles: np.ndarray) -> Tuple[float, np.ndarray]: + """QDiD ATT and quantile effects, matching qte::QDiD() exactly. + + ``qte(tau) = Q7(y11,tau) - [Q7(y10,tau) + Q7(y01,tau) - Q7(y00,tau)]``. The + ATT evaluates the control-group quantile functions at the treated + pre-period's own-sample ECDF ranks with type-7 quantiles - qte 1.3.1's + formula, which deviates in finite samples from the paper's k^QDID + transformation mean (population-equivalent; see the REGISTRY Note). + """ + q1 = _quantile_type7(cells["y11"], quantiles) + q0 = ( + _quantile_type7(cells["y10"], quantiles) + + _quantile_type7(cells["y01"], quantiles) + - _quantile_type7(cells["y00"], quantiles) + ) + ranks = _ecdf(cells["y10"], cells["y10"]) + att = float( + np.mean(cells["y11"]) + - ( + np.mean(cells["y10"]) + + np.mean(_quantile_type7(cells["y01"], ranks)) + - np.mean(_quantile_type7(cells["y00"], ranks)) + ) + ) + return att, q1 - q0 + + +def _interior_range(cells: Dict[str, np.ndarray]) -> Tuple[float, float]: + """Eq. (17) plug-in interior range for CiC quantile effects. + + ``q_lower = F_10(min y00)``, ``q_upper = F_10(max y00)``: quantile effects + are point-identified only inside ``(q_lower, q_upper)`` without the full + support condition (Corollary 3.1 / Theorem 5.3). + """ + y10 = cells["y10"] + q_lower = float(_ecdf(y10, np.array([cells["y00"][0]]))[0]) + q_upper = float(_ecdf(y10, np.array([cells["y00"][-1]]))[0]) + return q_lower, q_upper + + +def _parse_2x2_formula(formula: str, data: pd.DataFrame) -> Tuple[str, str, str]: + """Parse ``"outcome ~ treatment * time"`` style 2x2 formulas. + + Mirrors the DifferenceInDifferences formula grammar for the interaction + forms; covariate terms are rejected (covariates are deferred from v1). + """ + if "~" not in formula: + raise ValueError("Formula must contain '~' to separate outcome from predictors") + lhs, rhs = formula.split("~", 1) + outcome = lhs.strip() + rhs = rhs.strip() + + if "*" in rhs: + parts = [p.strip() for p in rhs.split("*")] + if len(parts) != 2: + raise ValueError("Currently only supports single interaction (treatment * time)") + treatment, time = parts + if "+" in time: + raise ValueError( + "Covariates are not supported by ChangesInChanges/QDiD (deferred from v1; " + "see the Melly-Santangelo covariate route in the methodology registry)." + ) + elif ":" in rhs: + terms = [t.strip() for t in rhs.split("+")] + interaction = None + mains: List[str] = [] + for term in terms: + if ":" in term: + if interaction is not None: + raise ValueError("Formula must contain exactly one interaction term") + interaction = term + else: + mains.append(term) + if interaction is None: + raise ValueError( + "Formula must include an interaction term (treatment * time or treatment:time)" + ) + pair = [p.strip() for p in interaction.split(":")] + if len(pair) != 2: + raise ValueError("Interaction term must involve exactly two variables") + treatment, time = pair + if sorted(mains) != sorted(pair): + raise ValueError( + "Covariates are not supported by ChangesInChanges/QDiD (deferred from v1; " + "the formula must be 'outcome ~ treatment + time + treatment:time')." + ) + else: + raise ValueError( + "Formula must include an interaction term (treatment * time or treatment:time)" + ) + + for name in (outcome, treatment, time): + if name not in data.columns: + raise ValueError(f"Column '{name}' from formula not found in data") + return outcome, treatment, time + + +# ============================================================================= +# Diagnostics +# ============================================================================= + + +def _check_support(cells: Dict[str, np.ndarray]) -> None: + """Warn on treated pre-period support outside the control pre-period range (CiC).""" + if cells["y10"][0] < cells["y00"][0] or cells["y10"][-1] > cells["y00"][-1]: + warnings.warn( + "Treated pre-period outcomes fall outside the control pre-period support " + "(Athey-Imbens Assumption 3.4 violated). The counterfactual distribution is " + "only partially identified (Corollary 3.1): quantile effects are reliable " + "only inside the reported (q_lower, q_upper) interior range, and the ATT " + "involves extrapolation at the support edges.", + UserWarning, + stacklevel=2, + ) + + +def _check_ties(cells: Dict[str, np.ndarray]) -> None: + """Warn on heavy ties (discrete-looking outcomes) in any cell.""" + max_share = 0.0 + for cell in cells.values(): + share = 1.0 - np.unique(cell).size / cell.size + max_share = max(max_share, share) + if max_share > _TIE_SHARE_WARN: + warnings.warn( + f"Outcome has heavy ties (up to {max_share:.0%} duplicate values within a " + "(group, period) cell), suggesting a discrete or mixed distribution. The " + "continuous CiC/QDiD machinery assumes continuously distributed outcomes " + "(Athey-Imbens Assumption 5.1(iii)); with discrete outcomes only bounds are " + "point-identified (Section 4, deferred) and the continuous formulas silently " + "deliver one endpoint of those bounds.", + UserWarning, + stacklevel=2, + ) + + +def _check_qdid_monotonicity(cells: Dict[str, np.ndarray], quantiles: np.ndarray) -> None: + """Warn when QDiD's counterfactual quantile curve is non-monotone (footnote 21).""" + cq = ( + _quantile_type7(cells["y10"], quantiles) + + _quantile_type7(cells["y01"], quantiles) + - _quantile_type7(cells["y00"], quantiles) + ) + if np.any(np.diff(cq) < -1e-12): + warnings.warn( + "QDiD's implied counterfactual quantile function is non-monotone on the " + "requested grid (Athey-Imbens footnote 21: the QDiD model places testable " + "restrictions on the data, and they appear violated here). Interpret the " + "quantile effects with caution; ChangesInChanges does not impose this " + "restriction and is the recommended estimator (p. 447).", + UserWarning, + stacklevel=2, + ) + + +# ============================================================================= +# Bootstrap +# ============================================================================= + + +def _bootstrap_replicates( + point_fn: Callable[[Dict[str, np.ndarray], np.ndarray], Tuple[float, np.ndarray]], + y: np.ndarray, + g: np.ndarray, + t: np.ndarray, + unit_ids: Optional[np.ndarray], + panel: bool, + n_bootstrap: int, + quantiles: np.ndarray, + rng: np.random.Generator, +) -> np.ndarray: + """Bootstrap replicate matrix, shape ``(n_bootstrap, 1 + K)`` (col 0 = ATT). + + Resampling matches qte 1.3.1: panel mode samples unit ids with replacement + (each unit's two periods travel together); repeated cross-section mode + draws one pooled row resample of the stacked two-period data (unstratified, + so cell sizes vary across draws). Replicates with an empty cell produce a + NaN row rather than an exception. + """ + n_cols = 1 + quantiles.shape[0] + out = np.full((n_bootstrap, n_cols), np.nan) + + if panel: + assert unit_ids is not None + # Pre-pivot to unit-level arrays: one (y_pre, y_post, group) triple per unit. + order = np.argsort(unit_ids, kind="stable") + uid, y_o, g_o, t_o = unit_ids[order], y[order], g[order], t[order] + pre_mask = t_o == 0 + # Balanced panel (enforced in fit): each unit has exactly one pre and one post row. + y_pre = y_o[pre_mask] + y_post = y_o[~pre_mask] + g_unit = g_o[pre_mask] + # uid is sorted, so pre/post slices align unit-by-unit. + assert np.array_equal(uid[pre_mask], uid[~pre_mask]) + n_units = y_pre.shape[0] + for b in range(n_bootstrap): + idx = rng.integers(0, n_units, n_units) + gb = g_unit[idx] + yb = np.concatenate([y_pre[idx], y_post[idx]]) + tb = np.concatenate([np.zeros(n_units), np.ones(n_units)]) + cells = _build_cells(yb, np.concatenate([gb, gb]), tb) + if cells is None: + continue + att_b, qte_b = point_fn(cells, quantiles)[:2] + out[b, 0] = att_b + out[b, 1:] = qte_b + else: + n_rows = y.shape[0] + for b in range(n_bootstrap): + idx = rng.integers(0, n_rows, n_rows) + cells = _build_cells(y[idx], g[idx], t[idx]) + if cells is None: + continue + att_b, qte_b = point_fn(cells, quantiles)[:2] + out[b, 0] = att_b + out[b, 1:] = qte_b + return out + + +def _bootstrap_inference( + replicates: np.ndarray, + qte_hat: np.ndarray, + n_bootstrap: int, + context: str, +) -> Tuple[float, np.ndarray, float, int]: + """SEs and the sup-t critical value from the replicate matrix (qte conventions). + + Returns ``(att_se, qte_ses, sup_t_crit, n_valid)``. SEs are SDs over finite + replicate rows (R ``sd``, ddof=1); if fewer than half the rows are finite, + all SEs and the critical value are NaN (bootstrap_utils gate). The sup-t + critical value ports qte's computeSE: an IQR-based scale ``sigmahalf`` per + quantile column (type-1 quantiles; SD floored at 1e-9 as fallback when any + column IQR is zero) and the hard-coded 0.95 type-1 quantile of the sup + statistics - independent of ``alpha`` by construction (qte parity). + """ + finite_rows = np.all(np.isfinite(replicates), axis=1) + n_valid = int(np.count_nonzero(finite_rows)) + warn_bootstrap_failure_rate(n_valid, n_bootstrap, context) + if n_valid < max(2, _MIN_VALID_REPLICATE_SHARE * n_bootstrap): + k = replicates.shape[1] - 1 + return np.nan, np.full(k, np.nan), np.nan, n_valid + + good = replicates[finite_rows] + with np.errstate(divide="ignore", invalid="ignore"): + ses = np.std(good, axis=0, ddof=1) + att_se = float(ses[0]) + qte_ses = ses[1:] + + qte_cols = good[:, 1:] + z_iqr = stats.norm.ppf(0.75) - stats.norm.ppf(0.25) + q75 = np.array([_quantile_type1(np.sort(col), np.array([0.75]))[0] for col in qte_cols.T]) + q25 = np.array([_quantile_type1(np.sort(col), np.array([0.25]))[0] for col in qte_cols.T]) + sigmahalf = (q75 - q25) / z_iqr + if np.any(sigmahalf == 0): + sigmahalf = np.maximum(qte_ses, 1e-9) + sup_stats = np.max(np.abs(qte_cols - qte_hat[None, :]) / sigmahalf[None, :], axis=1) + sup_t_crit = float(_quantile_type1(np.sort(sup_stats), np.array([0.95]))[0]) + + return att_se, qte_ses, sup_t_crit, n_valid + + +# ============================================================================= +# Shared fit pipeline +# ============================================================================= + + +def _fit_distributional( + est: Any, + data: pd.DataFrame, + outcome: Optional[str], + treatment: Optional[str], + time: Optional[str], + formula: Optional[str], + unit: Optional[str], + kind: str, +) -> ChangesInChangesResults: + """Shared fit pipeline for ChangesInChanges and QDiD (``kind`` in {"cic", "qdid"}).""" + # Re-validate hyperparameters (set_params may have mutated them since __init__). + _validate_all_params(est.get_params()) + quantiles = np.sort( + np.asarray(_DEFAULT_QUANTILES if est.quantiles is None else est.quantiles, dtype=float) + ) + + # ---- column resolution ------------------------------------------------- + if formula is not None: + outcome, treatment, time = _parse_2x2_formula(formula, data) + elif outcome is None or treatment is None or time is None: + raise ValueError( + "Must provide either 'formula' or all of 'outcome', 'treatment', and 'time'" + ) + used_cols = [outcome, treatment, time] + if est.panel: + if unit is None: + raise ValueError("'unit' is required when panel=True (unit identifier column)") + used_cols.append(unit) + for col in used_cols: + if col not in data.columns: + raise ValueError(f"Column '{col}' not found in data") + + # ---- NA handling ------------------------------------------------------- + frame = data[used_cols].copy() + n_before = len(frame) + frame = frame.dropna() + n_dropped = n_before - len(frame) + if n_dropped > 0: + warnings.warn( + f"Dropped {n_dropped} row(s) with missing values in " f"{used_cols} before estimation.", + UserWarning, + stacklevel=2, + ) + if len(frame) == 0: + raise ValueError("No observations remain after dropping missing values") + + validate_binary(frame[treatment].to_numpy(dtype=float), "treatment") + validate_binary(frame[time].to_numpy(dtype=float), "time") + + # ---- panel hygiene ----------------------------------------------------- + unit_ids: Optional[np.ndarray] = None + if est.panel: + if frame.duplicated(subset=[unit, time]).any(): + raise ValueError( + "panel=True requires at most one row per (unit, period); found duplicate " + f"('{unit}', '{time}') combinations." + ) + g_nunique = frame.groupby(unit)[treatment].nunique() + if (g_nunique > 1).any(): + bad = g_nunique[g_nunique > 1].index.tolist()[:5] + raise ValueError( + "The treatment-group indicator must be constant within unit in the 2x2 " + f"design (it marks group membership, not treatment receipt); units with " + f"varying values include {bad}." + ) + counts = frame.groupby(unit)[time].count() + incomplete = counts[counts < 2].index + if len(incomplete) > 0: + warnings.warn( + f"Dropped {len(incomplete)} unit(s) not observed in both periods " + "(balanced-panel requirement, matching qte's makeBalancedPanel).", + UserWarning, + stacklevel=2, + ) + frame = frame[~frame[unit].isin(incomplete)] + if len(frame) == 0: + raise ValueError("No balanced units remain after panel balancing") + unit_ids = frame[unit].to_numpy() + + y = frame[outcome].to_numpy(dtype=float) + g = frame[treatment].to_numpy(dtype=float).astype(np.int64) + t = frame[time].to_numpy(dtype=float).astype(np.int64) + + # ---- cells + diagnostics ----------------------------------------------- + cells = _split_cells(y, g, t) + _check_ties(cells) + if kind == "cic": + _check_support(cells) + q_lower, q_upper = _interior_range(cells) + else: + _check_qdid_monotonicity(cells, quantiles) + q_lower, q_upper = np.nan, np.nan + + # ---- point estimation --------------------------------------------------- + if kind == "cic": + att, qte, _ = _cic_point(cells, quantiles) + point_fn: Callable[..., Any] = _cic_point + context = "ChangesInChanges bootstrap" + else: + att, qte = _qdid_point(cells, quantiles) + point_fn = _qdid_point + context = "QDiD bootstrap" + + # ---- bootstrap ---------------------------------------------------------- + n_valid = 0 + if est.n_bootstrap > 0: + rng = np.random.default_rng(est.seed) + replicates = _bootstrap_replicates( + point_fn, y, g, t, unit_ids, est.panel, est.n_bootstrap, quantiles, rng + ) + # sup_t_crit is computed over ALL grid columns BEFORE the interior-range + # NaN overwrite below (qte has no interior guard; excluding guarded + # columns from the sup statistic would silently change c). + att_se, qte_ses, sup_t_crit, n_valid = _bootstrap_inference( + replicates, qte, est.n_bootstrap, context + ) + else: + att_se = np.nan + qte_ses = np.full(quantiles.shape[0], np.nan) + sup_t_crit = np.nan + + # ---- inference assembly --------------------------------------------------- + t_stat, p_value, conf_int = safe_inference(att, att_se, est.alpha) + t_stats, p_values, ci_lo, ci_hi = safe_inference_batch(qte, qte_ses, est.alpha) + + if kind == "cic": + exterior = ~((quantiles > q_lower) & (quantiles < q_upper)) + if np.any(exterior): + warnings.warn( + "Quantile effects at " + f"{[round(float(q), 4) for q in quantiles[exterior]]} lie outside the " + f"point-identified interior range ({q_lower:.4f}, {q_upper:.4f}) " + "(Athey-Imbens eq. 17 / Theorem 5.3). Point estimates are reported for " + "qte parity, but their inference fields are set to NaN.", + UserWarning, + stacklevel=2, + ) + qte_ses = qte_ses.copy() + qte_ses[exterior] = np.nan + t_stats[exterior] = np.nan + p_values[exterior] = np.nan + ci_lo[exterior] = np.nan + ci_hi[exterior] = np.nan + + quantile_effects = pd.DataFrame( + { + "quantile": quantiles, + "qte": qte, + "se": qte_ses, + "t_stat": t_stats, + "p_value": p_values, + "conf_low": ci_lo, + "conf_high": ci_hi, + } + ) + + results = ChangesInChangesResults( + att=att, + se=att_se if (np.isfinite(att_se) and att_se > 0) else np.nan, + t_stat=t_stat, + p_value=p_value, + conf_int=conf_int, + quantile_effects=quantile_effects, + q_lower=q_lower, + q_upper=q_upper, + sup_t_crit=sup_t_crit, + n_obs=len(frame), + cell_sizes={ + "control_pre": int(cells["y00"].size), + "control_post": int(cells["y01"].size), + "treated_pre": int(cells["y10"].size), + "treated_post": int(cells["y11"].size), + }, + n_bootstrap=est.n_bootstrap, + n_bootstrap_valid=n_valid, + panel=est.panel, + estimator=kind, + quantiles=quantiles, + alpha=est.alpha, + ) + est.results_ = results + est.is_fitted_ = True + return results + + +# ============================================================================= +# Estimators +# ============================================================================= + + +def _validate_quantiles(quantiles: Optional[Any]) -> None: + if quantiles is None: + return + arr = np.asarray(quantiles, dtype=float) + if arr.ndim != 1 or arr.size == 0: + raise ValueError(f"quantiles must be a non-empty 1-d array-like, got '{quantiles}'") + if not np.all(np.isfinite(arr)) or np.any(arr <= 0) or np.any(arr >= 1): + raise ValueError(f"quantiles must be finite and strictly inside (0, 1), got '{quantiles}'") + + +def _validate_n_bootstrap(n_bootstrap: Any) -> None: + if isinstance(n_bootstrap, bool) or not isinstance(n_bootstrap, (int, np.integer)): + raise ValueError(f"n_bootstrap must be a non-negative integer, got '{n_bootstrap}'") + if n_bootstrap < 0: + raise ValueError(f"n_bootstrap must be a non-negative integer, got '{n_bootstrap}'") + + +def _validate_alpha(alpha: Any) -> None: + if not isinstance(alpha, (int, float, np.floating)) or isinstance(alpha, bool): + raise ValueError(f"alpha must be a float strictly between 0 and 1, got '{alpha}'") + if not (0.0 < float(alpha) < 1.0): + raise ValueError(f"alpha must be a float strictly between 0 and 1, got '{alpha}'") + + +def _validate_panel(panel: Any) -> None: + if not isinstance(panel, (bool, np.bool_)): + raise ValueError(f"panel must be a boolean, got '{panel}'") + + +def _validate_seed(seed: Any) -> None: + if seed is None: + return + if isinstance(seed, bool) or not isinstance(seed, (int, np.integer)) or seed < 0: + raise ValueError(f"seed must be None or a non-negative integer, got '{seed}'") + + +def _validate_all_params(params: Dict[str, Any]) -> None: + """Validate the full hyperparameter dict (used by __init__, set_params, and fit).""" + _validate_quantiles(params["quantiles"]) + _validate_n_bootstrap(params["n_bootstrap"]) + _validate_alpha(params["alpha"]) + _validate_panel(params["panel"]) + _validate_seed(params["seed"]) + + +class ChangesInChanges: + """Changes-in-Changes estimator (Athey & Imbens 2006) for the 2x2 design. + + Estimates the ATT (eq. 36) and quantile treatment effects on the treated + (eq. 18) by building the treated group's counterfactual untreated outcome + distribution ``F_10(F_00^{-1}(F_01(y)))`` (Theorem 3.1). Point estimates + match ``qte::CiC()`` (R, v1.3.1) exactly; the empirical inverse CDF is the + paper's eq. (35)/(A.1) ceiling-order-statistic convention (R type-1). + + Inference is bootstrap-only (``n_bootstrap=0`` disables it and reports NaN + inference fields): panel mode resamples units with both periods together; + repeated cross-section mode draws a pooled row resample. Standard errors + are replicate SDs with symmetric normal-approximation intervals; + ``results_.sup_t_crit`` carries qte's sup-t critical value for uniform + bands at a fixed 95% level (independent of ``alpha``, matching qte). + + Parameters + ---------- + quantiles : array-like, optional + Quantile grid strictly inside (0, 1). Default: 0.05 to 0.95 in steps + of 0.05 (matches qte's ``probs``). + n_bootstrap : int, default=200 + Bootstrap replicates. 0 disables inference (NaN se/t/p/CI). + alpha : float, default=0.05 + Significance level for pointwise confidence intervals. + panel : bool, default=False + True when the same units are observed in both periods (requires + ``unit=`` at fit time; enforces a balanced panel). Affects only the + bootstrap resampling scheme - the point estimator uses the four + marginal cell distributions either way. + seed : int, optional + Seed for the bootstrap RNG (``numpy.random.default_rng``). + + Notes + ----- + Quantile effects are point-identified only on the eq. (17) interior range + ``(q_lower, q_upper)``; effects outside it keep their point estimates (qte + parity) but report NaN inference with a warning. + + Additive random group-time shocks (random effects at the group x period + level) BIAS the CiC estimator - unlike linear DiD, where they only + complicate inference - and are not detectable in a 2x2 design (Athey & + Imbens 2006, p. 476). With more than two groups/periods they are testable + (Theorem 6.4), but that extension is deferred. + + Covariates, discrete-outcome bounds, and analytical standard errors are + deferred from v1 and documented in docs/methodology/REGISTRY.md. + """ + + def __init__( + self, + quantiles: Optional[Any] = None, + n_bootstrap: int = 200, + alpha: float = 0.05, + panel: bool = False, + seed: Optional[int] = None, + ): + # Stored verbatim (sklearn-clone contract): quantiles=None resolves to the + # default grid at fit time, the raw None round-trips get_params(). + _validate_all_params( + { + "quantiles": quantiles, + "n_bootstrap": n_bootstrap, + "alpha": alpha, + "panel": panel, + "seed": seed, + } + ) + self.quantiles = quantiles + self.n_bootstrap = n_bootstrap + self.alpha = alpha + self.panel = panel + self.seed = seed + self.is_fitted_ = False + self.results_: Optional[ChangesInChangesResults] = None + + def get_params(self) -> Dict[str, Any]: + """Return constructor hyperparameters (raw values, round-trips ``__init__``).""" + return { + "quantiles": self.quantiles, + "n_bootstrap": self.n_bootstrap, + "alpha": self.alpha, + "panel": self.panel, + "seed": self.seed, + } + + def set_params(self, **params: Any) -> "ChangesInChanges": + """Set hyperparameters transactionally (a failing call mutates nothing).""" + valid = set(self.get_params()) + for key in params: + if key not in valid: + raise ValueError(f"Unknown parameter: {key}") + _validate_all_params({**self.get_params(), **params}) + for key, value in params.items(): + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: Optional[str] = None, + treatment: Optional[str] = None, + time: Optional[str] = None, + formula: Optional[str] = None, + unit: Optional[str] = None, + ) -> ChangesInChangesResults: + """Fit the CiC estimator on a 2x2 dataset. + + Parameters + ---------- + data : pd.DataFrame + Long-format data with one row per observation. + outcome, treatment, time : str, optional + Column names: continuous outcome, binary group indicator (1 = + treated group in BOTH periods), binary post-period indicator. + Required unless ``formula`` is given. + formula : str, optional + R-style 2x2 formula, e.g. ``"y ~ treated * post"``. Covariate + terms raise (deferred from v1). + unit : str, optional + Unit identifier column. Required when ``panel=True``; ignored + (documented) when ``panel=False``, matching qte's ``idname``. + """ + return _fit_distributional(self, data, outcome, treatment, time, formula, unit, "cic") + + +class QDiD: + """Quantile Difference-in-Differences comparison estimator (2x2 design). + + Applies DiD quantile-by-quantile: ``qte(tau) = Q(y11, tau) - [Q(y10, tau) + + Q(y01, tau) - Q(y00, tau)]`` with R type-7 (linear-interpolation) + quantiles, matching ``qte::QDiD()`` (v1.3.1) exactly - including its ATT + formula, which evaluates the control-group quantile functions at the + treated pre-period's own-sample ranks. This finite-sample form deviates + from the Athey-Imbens k^QDID transformation mean (they are + population-equivalent; see the labeled Note in the methodology registry). + + Athey & Imbens recommend :class:`ChangesInChanges` over QDiD (2006, + p. 447): QDiD's justifying model is not invariant to monotone + transformations of the outcome, forces the unobservable distribution to be + identical across all four cells, and places testable restrictions on the + data (a warning fires when the implied counterfactual quantile function is + non-monotone). QDiD's mean effect matches standard DiD's ATT in + population; the paper provides no asymptotic theory for QDiD, so inference + is a bootstrap convention shared with the qte package. + + Constructor parameters, fit signature, bootstrap behavior, and the results + container are identical to :class:`ChangesInChanges` (no interior-range + guard: eq. 17 has no QDiD analogue). + """ + + def __init__( + self, + quantiles: Optional[Any] = None, + n_bootstrap: int = 200, + alpha: float = 0.05, + panel: bool = False, + seed: Optional[int] = None, + ): + # Stored verbatim (sklearn-clone contract): quantiles=None resolves to the + # default grid at fit time, the raw None round-trips get_params(). + _validate_all_params( + { + "quantiles": quantiles, + "n_bootstrap": n_bootstrap, + "alpha": alpha, + "panel": panel, + "seed": seed, + } + ) + self.quantiles = quantiles + self.n_bootstrap = n_bootstrap + self.alpha = alpha + self.panel = panel + self.seed = seed + self.is_fitted_ = False + self.results_: Optional[ChangesInChangesResults] = None + + def get_params(self) -> Dict[str, Any]: + """Return constructor hyperparameters (raw values, round-trips ``__init__``).""" + return { + "quantiles": self.quantiles, + "n_bootstrap": self.n_bootstrap, + "alpha": self.alpha, + "panel": self.panel, + "seed": self.seed, + } + + def set_params(self, **params: Any) -> "QDiD": + """Set hyperparameters transactionally (a failing call mutates nothing).""" + valid = set(self.get_params()) + for key in params: + if key not in valid: + raise ValueError(f"Unknown parameter: {key}") + _validate_all_params({**self.get_params(), **params}) + for key, value in params.items(): + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: Optional[str] = None, + treatment: Optional[str] = None, + time: Optional[str] = None, + formula: Optional[str] = None, + unit: Optional[str] = None, + ) -> ChangesInChangesResults: + """Fit the QDiD estimator on a 2x2 dataset (see ChangesInChanges.fit).""" + return _fit_distributional(self, data, outcome, treatment, time, formula, unit, "qdid") diff --git a/diff_diff/changes_in_changes_results.py b/diff_diff/changes_in_changes_results.py new file mode 100644 index 000000000..1b48ac297 --- /dev/null +++ b/diff_diff/changes_in_changes_results.py @@ -0,0 +1,231 @@ +"""Results container for the ChangesInChanges (CiC) and QDiD estimators.""" + +from dataclasses import dataclass, field +from typing import Any, Dict, Tuple + +import numpy as np +import pandas as pd + +_ESTIMATOR_TITLES = { + "cic": "Changes-in-Changes (Athey & Imbens 2006) Results", + "qdid": "Quantile Difference-in-Differences (QDiD) Results", +} + + +@dataclass +class ChangesInChangesResults: + """Results for :class:`~diff_diff.changes_in_changes.ChangesInChanges` and + :class:`~diff_diff.changes_in_changes.QDiD`. + + The headline ``att``/``se``/``t_stat``/``p_value``/``conf_int`` fields carry + the mean effect; ``quantile_effects`` is a DataFrame with one row per + requested quantile (columns ``quantile``, ``qte``, ``se``, ``t_stat``, + ``p_value``, ``conf_low``, ``conf_high``). All inference derives from the + bootstrap (replicate-SD standard errors, symmetric normal-approximation + intervals at level ``alpha``); with ``n_bootstrap=0`` every inference field + is NaN. + + ``q_lower``/``q_upper`` bound the point-identified interior quantile range + for CiC (Athey-Imbens eq. 17; NaN for QDiD). ``sup_t_crit`` is the qte + package's sup-t critical value for uniform bands - computed at a FIXED 95% + level regardless of ``alpha`` (qte parity); see :meth:`uniform_bands`. + """ + + att: float + se: float + t_stat: float + p_value: float + conf_int: Tuple[float, float] + quantile_effects: pd.DataFrame + q_lower: float + q_upper: float + sup_t_crit: float + n_obs: int + cell_sizes: Dict[str, int] + n_bootstrap: int + n_bootstrap_valid: int + panel: bool + estimator: str + quantiles: np.ndarray = field(repr=False) + alpha: float = 0.05 + + # ------------------------------------------------------------------ + # convenience properties + # ------------------------------------------------------------------ + @property + def is_significant(self) -> bool: + """Whether the headline ATT is significant at level ``alpha`` (False on NaN).""" + return bool(np.isfinite(self.p_value) and self.p_value < self.alpha) + + @property + def significance_stars(self) -> str: + """Significance stars for the headline ATT p-value ('' when NaN).""" + from diff_diff.results import _get_significance_stars + + return "" if not np.isfinite(self.p_value) else _get_significance_stars(self.p_value) + + # ------------------------------------------------------------------ + # uniform bands + # ------------------------------------------------------------------ + def uniform_bands(self) -> pd.DataFrame: + """Simultaneous (sup-t) confidence bands over the quantile grid. + + ``qte +/- sup_t_crit * se`` per quantile, using the qte package's + IQR-scaled sup-t critical value at its hard-coded 95% level - the band + level does NOT follow ``alpha`` (qte parity). Rows whose ``se`` is NaN + (no bootstrap, failed replicate gate, or outside the CiC interior + range) get NaN bands. + """ + qe = self.quantile_effects + bands = pd.DataFrame( + { + "quantile": qe["quantile"], + "qte": qe["qte"], + "band_low": qe["qte"] - self.sup_t_crit * qe["se"], + "band_high": qe["qte"] + self.sup_t_crit * qe["se"], + } + ) + return bands + + # ------------------------------------------------------------------ + # serialization + # ------------------------------------------------------------------ + def to_dict(self) -> Dict[str, Any]: + """Flat headline dictionary (ATT inference, ranges, sizes, bootstrap metadata).""" + return { + "att": self.att, + "se": self.se, + "t_stat": self.t_stat, + "p_value": self.p_value, + "conf_int_lower": self.conf_int[0], + "conf_int_upper": self.conf_int[1], + "q_lower": self.q_lower, + "q_upper": self.q_upper, + "sup_t_crit": self.sup_t_crit, + "n_obs": self.n_obs, + "cell_sizes": dict(self.cell_sizes), + "n_bootstrap": self.n_bootstrap, + "n_bootstrap_valid": self.n_bootstrap_valid, + "panel": self.panel, + "estimator": self.estimator, + "alpha": self.alpha, + "inference_method": "bootstrap" if self.n_bootstrap > 0 else "none", + } + + def to_dataframe(self, level: str = "quantiles") -> pd.DataFrame: + """Return the quantile-effects table or the one-row ATT summary. + + Parameters + ---------- + level : {"quantiles", "att"} + ``"quantiles"`` (default) returns a copy of ``quantile_effects``; + ``"att"`` returns a single-row frame with the headline fields. + """ + if level == "quantiles": + return self.quantile_effects.copy() + if level == "att": + return pd.DataFrame( + [ + { + "att": self.att, + "se": self.se, + "t_stat": self.t_stat, + "p_value": self.p_value, + "conf_low": self.conf_int[0], + "conf_high": self.conf_int[1], + "n_obs": self.n_obs, + } + ] + ) + raise ValueError(f"level must be 'quantiles' or 'att', got '{level}'") + + # ------------------------------------------------------------------ + # text summary + # ------------------------------------------------------------------ + def summary(self) -> str: + """Fixed-width text summary: headline ATT block plus the quantile-effects table.""" + from diff_diff.results import _get_significance_stars + + ci_pct = int(round((1 - self.alpha) * 100)) + width = 88 + bar = "=" * width + dash = "-" * width + + def _fmt(x: Any, nd: int = 4) -> str: + try: + xf = float(x) + except (TypeError, ValueError): + return "" + return "" if np.isnan(xf) else f"{xf:.{nd}f}" + + mode = "panel (unit block bootstrap)" if self.panel else "repeated cross-section" + cs = self.cell_sizes + lines = [ + bar, + _ESTIMATOR_TITLES.get(self.estimator, "Distributional DiD Results").center(width), + bar, + f"Observations: {self.n_obs} Mode: {mode}", + ( + f"Cells: control pre={cs.get('control_pre')}, " + f"control post={cs.get('control_post')}, " + f"treated pre={cs.get('treated_pre')}, " + f"treated post={cs.get('treated_post')}" + ), + ] + if self.n_bootstrap > 0: + lines.append( + f"Inference: bootstrap ({self.n_bootstrap_valid}/{self.n_bootstrap} " + "valid replicates), replicate-SD SEs, normal-approximation intervals" + ) + else: + lines.append("Inference: disabled (n_bootstrap=0); all inference fields are NaN") + if self.estimator == "cic" and np.isfinite(self.q_lower) and np.isfinite(self.q_upper): + lines.append( + f"Point-identified interior quantile range (eq. 17): " + f"({self.q_lower:.4f}, {self.q_upper:.4f})" + ) + + stars = "" if np.isnan(self.p_value) else _get_significance_stars(self.p_value) + lines.extend( + [ + dash, + f"{'':>10} {'Estimate':>10} {'Std.Err':>10} {'t':>8} {'P>|t|':>8}" + f" [{ci_pct}% Conf. Int.]", + dash, + f"{'ATT':>10} {_fmt(self.att):>10} {_fmt(self.se):>10}" + f" {_fmt(self.t_stat, 2):>8} {_fmt(self.p_value, 3):>8}" + f" [{_fmt(self.conf_int[0]):>9}, {_fmt(self.conf_int[1]):>9}] {stars}", + "", + "Quantile treatment effects:", + dash, + ] + ) + for _, r in self.quantile_effects.iterrows(): + p = r["p_value"] + row_stars = "" if pd.isna(p) else _get_significance_stars(float(p)) + lines.append( + f"{r['quantile']:>10.2f} {_fmt(r['qte']):>10} {_fmt(r['se']):>10}" + f" {_fmt(r['t_stat'], 2):>8} {_fmt(r['p_value'], 3):>8}" + f" [{_fmt(r['conf_low']):>9}, {_fmt(r['conf_high']):>9}] {row_stars}" + ) + lines.append(bar) + lines.append("Signif. codes: *** p<0.001, ** p<0.01, * p<0.05") + return "\n".join(lines) + + def print_summary(self) -> None: + """Print :meth:`summary` to stdout.""" + print(self.summary()) + + def __repr__(self) -> str: + att_s = "nan" if np.isnan(self.att) else f"{self.att:.4f}" + se_s = "nan" if np.isnan(self.se) else f"{self.se:.4f}" + return ( + "ChangesInChangesResults(" + f"estimator={self.estimator!r}, ATT={att_s}, SE={se_s}, " + f"n_quantiles={len(self.quantile_effects)}, " + f"panel={self.panel}, n_bootstrap={self.n_bootstrap})" + ) + + +# QDiD shares the container; the ``estimator`` field distinguishes the two. +QDiDResults = ChangesInChangesResults diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index 3039d1136..36fde4994 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -972,6 +972,70 @@ print(results.event_study) # per-horizon coefficients print(results.pooled) # pooled pre (placebo) / post (ATT) rows ``` +### ChangesInChanges + +Changes-in-Changes (Athey & Imbens 2006) for the canonical 2x2 design with continuous outcomes. Recovers the treated group's full counterfactual outcome distribution `F_10(F_00^{-1}(F_01(y)))` and reports the ATT plus quantile treatment effects on a grid (default 0.05-0.95 by 0.05, matching R `qte`). The model is invariant to monotone transformations of the outcome and, with continuous data, places no testable restrictions. Point estimation matches `qte::CiC()` (v1.3.1) exactly: R type-1 (ceiling-order-statistic) quantiles - the paper's empirical-inverse convention - throughout, no smoothing anywhere. Inference is bootstrap-only in this release: panel mode resamples units (both periods together), repeated cross-section mode draws a pooled row resample; SEs are replicate SDs with symmetric normal-approximation CIs, plus a sup-t critical value for uniform bands at a FIXED 95% level (qte parity - does not follow `alpha`). Quantile effects outside the point-identified interior range keep their point estimates but report NaN inference with a warning. Deferred (documented in REGISTRY.md): covariates, discrete-outcome bounds (a ties warning fires on discrete-looking outcomes), analytical SEs, staggered designs, treatment-on-controls. Note: additive random group-time shocks BIAS CiC (not just its inference) and are undetectable in a 2x2 design (Athey-Imbens p. 476). + +```python +ChangesInChanges( + quantiles: array-like | None = None, # Quantile grid strictly inside (0,1); None -> 0.05..0.95 step 0.05 (qte default) + n_bootstrap: int = 200, # Bootstrap replicates; 0 disables inference (NaN se/t/p/CI) + alpha: float = 0.05, # Pointwise CI level (uniform bands stay fixed at 95%) + panel: bool = False, # True: same units both periods (requires unit=); affects resampling only + seed: int | None = None, # Bootstrap RNG seed (numpy default_rng) +) +``` + +**fit() parameters:** + +```python +cic.fit( + data: pd.DataFrame, + outcome: str = None, # Continuous outcome column + treatment: str = None, # Binary group indicator (1 = treated group in BOTH periods) + time: str = None, # Binary post-period indicator + formula: str = None, # Alternative: "y ~ treated * post" (covariate terms raise) + unit: str = None, # Unit id; required when panel=True, ignored (documented) otherwise +) -> ChangesInChangesResults +``` + +**Alias:** `CiC` + +**Usage:** + +```python +from diff_diff import ChangesInChanges + +cic = ChangesInChanges(n_bootstrap=200, seed=42) +results = cic.fit(data, outcome='y', treatment='treated', time='post') +results.print_summary() +print(results.quantile_effects) # per-quantile QTE table +print(results.uniform_bands()) # sup-t simultaneous bands (fixed 95%) +``` + +### QDiD + +Quantile Difference-in-Differences comparison estimator (Athey & Imbens 2006, Section 3.3) for the 2x2 design: `QTE(tau) = Q(y11,tau) - [Q(y10,tau) + Q(y01,tau) - Q(y00,tau)]` with R type-7 linear-interpolation quantiles, matching `qte::QDiD()` (v1.3.1) exactly - including its ATT formula (control-group quantile functions evaluated at treated pre-period own-sample ranks; population-equivalent to the paper's k^QDID transformation but a different finite-sample estimator, see the REGISTRY.md Note). The paper recommends ChangesInChanges over QDiD: QDiD's justifying model is not scale-invariant, forces identical unobservable distributions in all four cells, and places testable restrictions on the data (a warning fires when the implied counterfactual quantile function is non-monotone). QDiD's mean effect equals standard DiD's ATT in population. Constructor, fit signature, bootstrap machinery, and results container are identical to ChangesInChanges (no interior-range guard; `q_lower`/`q_upper` are NaN). + +```python +QDiD( + quantiles: array-like | None = None, # Same defaults as ChangesInChanges + n_bootstrap: int = 200, + alpha: float = 0.05, + panel: bool = False, + seed: int | None = None, +) +``` + +**Usage:** + +```python +from diff_diff import QDiD + +qdid = QDiD(n_bootstrap=200, seed=42) +results = qdid.fit(data, outcome='y', treatment='treated', time='post') +``` + ### TROP Triply Robust Panel estimator (Athey, Imbens, Qu & Viviano 2025). Combines nuclear norm regularization, distance-based unit weights, and time decay weights. @@ -1601,6 +1665,10 @@ Per-horizon event-study results container for `HeterogeneousAdoptionDiD` with `a **Methods:** `summary()`, `print_summary()`, `to_dict()`, `to_dataframe()` +### ChangesInChangesResults + +Results container shared by `ChangesInChanges` and `QDiD` (the `estimator` field is `"cic"` or `"qdid"`; `QDiDResults` is an alias of this class). Flat-native headline fields `att`, `se`, `t_stat`, `p_value`, `conf_int`; `quantile_effects` is a DataFrame with columns `quantile`, `qte`, `se`, `t_stat`, `p_value`, `conf_low`, `conf_high`. `q_lower`/`q_upper` bound the CiC point-identified interior quantile range (NaN for QDiD); `sup_t_crit` is the qte sup-t critical value backing `uniform_bands()` (fixed 95% level). Also carries `n_obs`, `cell_sizes`, `n_bootstrap`, `n_bootstrap_valid`, `panel`, `quantiles`, `alpha`. Methods: `summary()`, `print_summary()`, `to_dict()`, `to_dataframe(level="quantiles"|"att")`, `uniform_bands()`. All inference flows through `safe_inference`/`safe_inference_batch` (joint-NaN contract; `n_bootstrap=0` yields NaN inference everywhere). + ### TROPResults | Attribute | Type | Description | @@ -2160,7 +2228,7 @@ sd_female, data_female = sd.subpopulation(data, mask=lambda df: df['sex'] == 'F' **Key features:** - Taylor Series Linearization (TSL) variance with strata + PSU + FPC -- Replicate weight variance: BRR, Fay's BRR, JK1, JKn, SDR (13 of 20 estimators, including dCDH) +- Replicate weight variance: BRR, Fay's BRR, JK1, JKn, SDR (13 of 22 estimators, including dCDH) - Survey-aware bootstrap: multiplier at PSU (Hall-Mammen wild; dCDH, staggered) or Rao-Wu rescaled (SunAbraham, SyntheticDiD, TROP). SyntheticDiD bootstrap composes Rao-Wu rescaled per-draw weights with the weighted Frank-Wolfe variant of `_sc_weight_fw` (PR #355): each draw solves `min ||A·diag(rw)·ω - b||² + ζ²·Σ rw_i ω_i²` and composes `ω_eff = rw·ω/Σ(rw·ω)` for the SDID estimator. Pweight-only fits use constant `rw = w_control`; full designs use Rao-Wu. SDID's placebo (stratified permutation + weighted FW) and jackknife (PSU-level LOO with stratum aggregation, Rust & Rao 1996) paths also support pweight-only and full strata/PSU/FPC designs - DEFF diagnostics, subpopulation analysis, weight trimming (`trim_weights`) - Repeated cross-sections: `CallawaySantAnna(panel=False)` diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt index 388318eff..f547c0763 100644 --- a/diff_diff/guides/llms.txt +++ b/diff_diff/guides/llms.txt @@ -2,7 +2,7 @@ > A Python library for Difference-in-Differences (DiD) causal inference analysis. Provides sklearn-like estimators with statsmodels-style summary output for econometric analysis. -diff-diff offers 20 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP. +diff-diff offers 22 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP. - Install: `pip install diff-diff` - License: MIT @@ -70,6 +70,8 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")` - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html): Wooldridge (2023, 2025) ETWFE — saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias: ETWFE - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html): Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting); variance- or equally-weighted ATT, premean differencing, pooled pre/post, fast. Absorbing by default; non-absorbing (reversible) treatment via `non_absorbing="first_entry"` (Eq. 12) or `"effect_stabilization"` (Eq. 13, window `L`). Complex-survey designs (pweight + stratified-PSU TSL SEs) on the default path via `fit(survey_design=...)`. +- [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: recovers the treated group's full counterfactual outcome distribution and quantile treatment effects (ATT + QTE grid) via the CDF transformation `F_10(F_00^{-1}(F_01(y)))`; invariant to monotone outcome transformations; bootstrap inference (panel or repeated cross-section resampling); point parity with R `qte::CiC()`. Continuous outcomes, no covariates in this release. Alias `CiC`. +- [QDiD](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) quantile DiD comparison estimator (additive quantile-by-quantile DiD, matching R `qte::QDiD()`); same bootstrap machinery as ChangesInChanges. The paper recommends CiC over QDiD (scale-dependent model with testable restrictions; a non-monotonicity warning fires when violated). - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html): Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings ## Diagnostics and Sensitivity Analysis diff --git a/docs/api/changes_in_changes.rst b/docs/api/changes_in_changes.rst new file mode 100644 index 000000000..8c5851fa7 --- /dev/null +++ b/docs/api/changes_in_changes.rst @@ -0,0 +1,182 @@ +Changes-in-Changes (CiC) and Quantile DiD +========================================= + +Distributional difference-in-differences for the canonical 2x2 design (two +groups, two periods) with continuous outcomes, from Athey & Imbens (2006). +Where standard DiD delivers a mean effect, these estimators recover the entire +counterfactual outcome distribution of the treated group, yielding quantile +treatment effects on the treated alongside the ATT. + +``ChangesInChanges`` (alias ``CiC``) builds the counterfactual distribution +``F_10(F_00^{-1}(F_01(y)))``: each treated pre-period outcome is ranked in the +control pre-period distribution and pushed through the control post-period +quantile function. The model is invariant to monotone transformations of the +outcome (levels vs logs give consistent answers) and places no testable +restrictions on continuous data. + +``QDiD`` is the quantile-by-quantile DiD comparison estimator the same paper +formalizes: it adds the control group's over-time quantile change to the +treated pre-period quantile. Athey & Imbens recommend CiC over QDiD - QDiD's +justifying model is not scale-invariant and imposes testable restrictions (a +warning fires when they appear violated). + +.. note:: + + Point estimation matches the R ``qte`` package (v1.3.1) exactly - CiC via + R type-1 (ceiling-order-statistic) quantiles, the paper's empirical-inverse + convention, and QDiD via qte's additive type-7 form (see the labeled Note + in ``docs/methodology/REGISTRY.md`` on its finite-sample deviation from the + paper's transformation form). Inference is bootstrap-only in this release + (``n_bootstrap=200`` default, seedable): panel mode resamples units with + both periods together, repeated cross-section mode draws a pooled row + resample; SEs are replicate SDs with symmetric normal-approximation + intervals, plus qte's sup-t critical value for uniform bands at a fixed + 95% level (independent of ``alpha``). Covariates, discrete-outcome bounds, + analytical standard errors, and staggered designs are deferred - see + ``docs/methodology/REGISTRY.md`` for the documented scope. + +**When to use ChangesInChanges:** + +- Exactly two groups and two periods, continuous outcome, and you care about + effect heterogeneity across the outcome distribution (which quantiles moved, + not just the mean) +- You want results invariant to monotone rescaling of the outcome +- CiC quantile effects are point-identified on the interior range where the + treated pre-period distribution overlaps the control pre-period support; + effects outside it keep their point estimates but report NaN inference + +**Reference:** Athey, S., & Imbens, G. W. (2006). Identification and Inference +in Nonlinear Difference-in-Differences Models. *Econometrica*, 74(2), 431-497. + +.. module:: diff_diff.changes_in_changes + +ChangesInChanges +---------------- + +Main changes-in-changes estimator class. + +.. autoclass:: diff_diff.ChangesInChanges + :no-index: + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~ChangesInChanges.fit + ~ChangesInChanges.get_params + ~ChangesInChanges.set_params + +QDiD +---- + +Quantile difference-in-differences comparison estimator. + +.. autoclass:: diff_diff.QDiD + :no-index: + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~QDiD.fit + ~QDiD.get_params + ~QDiD.set_params + +ChangesInChangesResults +----------------------- + +Results container shared by both estimators (the ``estimator`` field records +which produced it; ``QDiDResults`` is an alias of this class). + +.. autoclass:: diff_diff.changes_in_changes_results.ChangesInChangesResults + :no-index: + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~ChangesInChangesResults.summary + ~ChangesInChangesResults.print_summary + ~ChangesInChangesResults.to_dataframe + ~ChangesInChangesResults.to_dict + ~ChangesInChangesResults.uniform_bands + +Example Usage +------------- + +Estimate quantile treatment effects in a 2x2 design:: + + import numpy as np + import pandas as pd + from diff_diff import ChangesInChanges + + rng = np.random.default_rng(0) + n = 400 + treated = np.repeat([1, 0], n // 2) + u = rng.normal(0, 1, n) + y_pre = u + rng.normal(0, 0.3, n) + y_post = u + 0.5 + rng.normal(0, 0.3, n) + treated * (0.8 + 0.4 * (u > 0)) + data = pd.DataFrame({ + "unit": np.tile(np.arange(n), 2), + "post": np.repeat([0, 1], n), + "treated": np.tile(treated, 2), + "y": np.concatenate([y_pre, y_post]), + }) + + cic = ChangesInChanges(n_bootstrap=200, seed=42) + results = cic.fit(data, outcome="y", treatment="treated", time="post") + results.print_summary() + + # Quantile effects table and simultaneous bands + qte = results.to_dataframe("quantiles") + bands = results.uniform_bands() + +Panel mode (same units in both periods) changes only the bootstrap:: + + cic_panel = ChangesInChanges(n_bootstrap=200, seed=42, panel=True) + results_panel = cic_panel.fit( + data, outcome="y", treatment="treated", time="post", unit="unit" + ) + +QDiD as a comparison estimator:: + + from diff_diff import QDiD + + qdid = QDiD(n_bootstrap=200, seed=42) + results_qdid = qdid.fit(data, outcome="y", treatment="treated", time="post") + +Comparison with related estimators +---------------------------------- + +.. list-table:: + :header-rows: 1 + + * - Estimator + - Design + - Output + - Key assumption + * - ``DifferenceInDifferences`` + - 2x2 + - Mean ATT + - Parallel trends (additive, scale-dependent) + * - ``ChangesInChanges`` + - 2x2 + - ATT + quantile effects + - ``h(u, t)`` monotone in scalar unobservable; ``U`` time-invariant within groups + * - ``QDiD`` + - 2x2 + - ATT + quantile effects + - Additive quantile model (scale-dependent, testable restrictions) + * - ``CallawaySantAnna`` + - Staggered + - Mean ATT(g, t) + - Parallel trends conditional on covariates diff --git a/docs/api/index.rst b/docs/api/index.rst index 145ba611c..44a2bd0e7 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -31,6 +31,8 @@ Core estimator classes for DiD analysis: diff_diff.SpilloverDiD diff_diff.WooldridgeDiD diff_diff.LPDiD + diff_diff.ChangesInChanges + diff_diff.QDiD diff_diff.BaconDecomposition diff_diff.StaggeredTripleDifference @@ -72,6 +74,7 @@ Result containers returned by estimators: diff_diff.BaconDecompositionResults diff_diff.wooldridge_results.WooldridgeDiDResults diff_diff.lpdid_results.LPDiDResults + diff_diff.changes_in_changes_results.ChangesInChangesResults diff_diff.Comparison2x2 diff_diff.StaggeredTripleDiffResults diff_diff.TWFEWeightsResult @@ -321,6 +324,7 @@ Estimators spillover wooldridge_etwfe lpdid + changes_in_changes bacon Infrastructure diff --git a/docs/choosing_estimator.rst b/docs/choosing_estimator.rst index 045913a8b..fe6376da0 100644 --- a/docs/choosing_estimator.rst +++ b/docs/choosing_estimator.rst @@ -39,6 +39,7 @@ Start here and follow the questions: 4. **Do you have panel data?** (Multiple observations per unit over time) - **No** → Use :class:`~diff_diff.DifferenceInDifferences` (basic 2x2) + - **No, and you care about effect heterogeneity across the outcome distribution** → Use :class:`~diff_diff.ChangesInChanges` (2x2 quantile treatment effects, invariant to monotone outcome rescaling; works with panel data too - ``panel=True`` changes only the bootstrap). :class:`~diff_diff.QDiD` is the quantile-DiD comparison estimator; Athey & Imbens (2006) recommend CiC over it - **Yes** → Go to question 5 5. **Do you need period-specific effects?** (Event study design) @@ -133,6 +134,14 @@ Quick Reference - Fast staggered (absorbing) event studies without negative weighting - Parallel trends, no anticipation; absorbing treatment - Event-study path + pooled pre/post ATT + * - ``ChangesInChanges`` + - 2x2 distributional effects (which quantiles moved, not just the mean) + - h(u, t) monotone in a scalar unobservable; U time-invariant within groups + - ATT + quantile treatment effects (bootstrap inference) + * - ``QDiD`` + - 2x2 quantile-DiD comparison alongside ChangesInChanges + - Additive quantile model (scale-dependent, testable restrictions) + - ATT + quantile treatment effects (bootstrap inference) * - ``BaconDecomposition`` - TWFE diagnostic - (diagnostic tool) @@ -893,6 +902,11 @@ estimation. The depth of support varies by estimator and variance method: - Full (Binder TSL) - -- - -- + * - ``ChangesInChanges`` / ``QDiD`` + - -- + - -- + - -- + - -- * - ``SpilloverDiD`` - pweight only - Full (Binder TSL + Conley) diff --git a/docs/doc-deps.yaml b/docs/doc-deps.yaml index 51b8b8548..adc7a72fc 100644 --- a/docs/doc-deps.yaml +++ b/docs/doc-deps.yaml @@ -65,6 +65,9 @@ groups: lpdid: - diff_diff/lpdid.py - diff_diff/lpdid_results.py + changes_in_changes: + - diff_diff/changes_in_changes.py + - diff_diff/changes_in_changes_results.py visualization: - diff_diff/visualization/__init__.py - diff_diff/visualization/_common.py @@ -619,6 +622,34 @@ sources: - path: docs/survey-roadmap.md type: user_guide + # ── ChangesInChanges + QDiD (changes_in_changes group) ──────────── + + diff_diff/changes_in_changes.py: + drift_risk: medium + docs: + - path: docs/methodology/REGISTRY.md + section: "ChangesInChanges (CiC)" + type: methodology + - path: docs/methodology/papers/athey-imbens-2006-review.md + type: methodology + - path: docs/api/changes_in_changes.rst + type: api_reference + - path: README.md + section: "Estimators (one-line catalog entry)" + type: user_guide + - path: docs/references.rst + type: user_guide + - path: diff_diff/guides/llms-full.txt + section: "ChangesInChanges" + type: user_guide + - path: diff_diff/guides/llms.txt + section: "Estimators" + type: user_guide + - path: docs/choosing_estimator.rst + type: user_guide + - path: docs/r_comparison.rst + type: user_guide + # ── TROP (trop group) ────────────────────────────────────────────── diff_diff/trop.py: diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index f70c09068..681c7548a 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -2,7 +2,7 @@ This document provides the academic foundations and key implementation requirements for each estimator in diff-diff. It serves as a reference for contributors and users who want to understand the theoretical basis of the methods. -**Result-class field naming.** Headline scalar inference fields appear under one of four native naming patterns: flat `att` / `se` / `conf_int` / `p_value` / `t_stat` (`DiDResults`, `SyntheticDiDResults`, `TROPResults`, `TripleDifferenceResults`, `HeterogeneousAdoptionDiDResults`); `overall_*` (`CallawaySantAnnaResults` and the rest of the staggered family); `overall_att_*` (`ContinuousDiDResults`, where `att` and `acrt` are parallel response curves); and `avg_*` (`MultiPeriodDiDResults`). Result classes in the prefixed `overall_*` / `overall_att_*` / `avg_*` families additionally expose the flat `att` / `se` / `conf_int` / `p_value` / `t_stat` names as read-only `@property` aliases over their canonical fields, for adapter / external-consumer compatibility (see PR for v3.3.3, motivated by `balance.interop.diff_diff`). The flat-native classes (`DiDResults`, `SyntheticDiDResults`, `TROPResults`, `TripleDifferenceResults`, `HeterogeneousAdoptionDiDResults`) already carry these names as native dataclass fields and are unchanged by this contract. `ContinuousDiDResults` further exposes `overall_*` aliases pointing at the ATT side (so `result.overall_se` reads `result.overall_att_se`, etc.). The native field is canonical for documentation, semantics, and computation — aliases are pure read-throughs and inherit the `safe_inference()` joint-NaN consistency contract automatically. Because aliases are `@property` descriptors (not dataclass fields), they do NOT appear in `dataclasses.fields()` or `dataclasses.asdict()` output, and assignment to an alias raises `AttributeError`; serializers and field-walkers continue to see only the canonical field set. +**Result-class field naming.** Headline scalar inference fields appear under one of four native naming patterns: flat `att` / `se` / `conf_int` / `p_value` / `t_stat` (`DiDResults`, `SyntheticDiDResults`, `TROPResults`, `TripleDifferenceResults`, `HeterogeneousAdoptionDiDResults`, `ChangesInChangesResults`); `overall_*` (`CallawaySantAnnaResults` and the rest of the staggered family); `overall_att_*` (`ContinuousDiDResults`, where `att` and `acrt` are parallel response curves); and `avg_*` (`MultiPeriodDiDResults`). Result classes in the prefixed `overall_*` / `overall_att_*` / `avg_*` families additionally expose the flat `att` / `se` / `conf_int` / `p_value` / `t_stat` names as read-only `@property` aliases over their canonical fields, for adapter / external-consumer compatibility (see PR for v3.3.3, motivated by `balance.interop.diff_diff`). The flat-native classes (`DiDResults`, `SyntheticDiDResults`, `TROPResults`, `TripleDifferenceResults`, `HeterogeneousAdoptionDiDResults`, `ChangesInChangesResults`) already carry these names as native dataclass fields and are unchanged by this contract. `ContinuousDiDResults` further exposes `overall_*` aliases pointing at the ATT side (so `result.overall_se` reads `result.overall_att_se`, etc.). The native field is canonical for documentation, semantics, and computation — aliases are pure read-throughs and inherit the `safe_inference()` joint-NaN consistency contract automatically. Because aliases are `@property` descriptors (not dataclass fields), they do NOT appear in `dataclasses.fields()` or `dataclasses.asdict()` output, and assignment to an alias raises `AttributeError`; serializers and field-walkers continue to see only the canonical field set. ## Table of Contents @@ -27,6 +27,7 @@ This document provides the academic foundations and key implementation requireme - [StaggeredTripleDifference](#staggeredtripledifference) - [TROP](#trop) - [HeterogeneousAdoptionDiD](#heterogeneousadoptiondid) + - [ChangesInChanges (CiC) + QDiD](#changesinchanges-cic) - [SpilloverDiD](#spilloverdid) 4. [Regression Discontinuity](#regression-discontinuity) - [RegressionDiscontinuity](#regressiondiscontinuity) @@ -3578,6 +3579,76 @@ Journal published Senate anchors under `masspoints="off"`. --- +## ChangesInChanges (CiC) + +**Primary source:** Athey, S., & Imbens, G. W. (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. *Econometrica*, 74(2), 431-497. https://doi.org/10.1111/j.1468-0262.2006.00668.x + +Full equation-level review (all equation/theorem/page pins below refer to the published version): `docs/methodology/papers/athey-imbens-2006-review.md`. Companion reviews for the deferred extensions: `callaway-li-oka-2018-review.md` (panel QTT / bootstrap validity machinery), `melly-santangelo-2015-review.md` (covariates), `ciaccio-2024-review.md` (staggered). + +**Model (Sections 2-3):** two groups x two periods; untreated outcomes generated by `Y^N = h(U, T)` with `h` strictly increasing in a scalar unobservable U (Assumptions 3.1-3.2), `U ⊥ T | G` (Assumption 3.3, time-invariance within groups), and support inclusion `U_1 ⊆ U_0` (Assumption 3.4, relaxable per Corollary 3.1). The counterfactual distribution of the treated group's untreated post-period outcome is `F_{Y^N,11}(y) = F_10(F_00^{-1}(F_01(y)))` (Theorem 3.1, eq. 9). With continuous outcomes the model imposes no testable restrictions and is invariant to monotone transformations of the outcome (p. 437-439). + +**Key implementation requirements:** + +*Assumption checks / warnings:* +- All four (group, period) cells non-empty (Assumption 5.1(ii)) -> `ValueError`. +- Support check: treated pre-period outcomes outside the control pre-period range (Assumption 3.4 / Corollary 3.1) -> `UserWarning`; quantile effects remain point-identified only on the eq. (17) interior range. +- Heavy ties (> 10% duplicate values within a cell; library heuristic - the paper's Assumption 5.1(iii) is continuity, with no finite-sample ties rule, and this threshold is the library's operationalization of "discrete-looking" for the mixed continuous/discrete edge case) -> `UserWarning` citing the Section 4 bounds deferral: the continuous machinery applied to discrete data silently delivers one endpoint of the Athey-Imbens bounds, not a point estimate. +- Docstring warning (not detectable in a 2x2 design): additive random group-time shocks BIAS CiC - unlike linear DiD where they only complicate inference; testable only with more than two groups/periods via Theorem 6.4 (p. 476; deferred). + +*Estimator equations (eqs. 34-36 and 17-18, as implemented):* + + cf_i = Q1( y01, ECDF_y00(y10_i) ) counterfactual draws (eq. 36 inner term) + ATT = mean(y11) - mean(cf) (eq. 36) + QTE(tau) = Q1(y11, tau) - Q1(cf, tau) (eq. 18 plug-in) + +where `Q1` is the R type-1 quantile - the paper's eq. (35)/(A.1) inf-based ceiling-order-statistic inverse (`inf{y : F_hat(y) >= q}` = `Y_(ceil(Nq))`, `Q1(., 0)` = sample minimum) - and `ECDF` is the eq. (34) `<=`-semantics empirical CDF. The implementation ports R `quantile.default` type-1 arithmetic exactly, INCLUDING its `4 * .Machine$double.eps` fuzz on the index computation (`diff_diff/changes_in_changes.py::_quantile_type1`); this is where cross-language implementations diverge, and the type-1 micro-fixtures in `benchmarks/data/qte_golden.json` pin it bit-exactly against R. +- **Note:** the estimator is deliberately smoothing-free (p. 451): empirical CDFs and order statistics only, no interpolating quantile definitions anywhere in the CiC pipeline. This yields exact monotone-transform equivariance (locked by `tests/test_methodology_changes_in_changes.py`). + +*Interior-range guard (eq. 17 / Theorem 5.3):* `q_lower = ECDF_y10(min y00)`, `q_upper = ECDF_y10(max y00)`; requested quantiles outside the open interval `(q_lower, q_upper)` keep their point estimates (qte parity - `qte::CiC()` has no guard) but report NaN inference with a `UserWarning` (Theorem 5.3's asymptotics are interior-only). + +*Standard errors:* +- Bootstrap-only in v1 (`n_bootstrap=200` default, `seed` param; `n_bootstrap=0` -> joint-NaN inference via `safe_inference`/`safe_inference_batch`). +- **Note (bootstrap is a qte-parity convention, not a paper prescription):** Athey-Imbens 2006 contains no bootstrap - its inference is analytical (influence functions, Theorems 5.1-5.7; deferred). The bootstrap convention matches `qte` 1.3.1 exactly: panel mode resamples units with both periods traveling together; repeated cross-section mode draws a single pooled row resample of the stacked two-period data (unstratified - replicate cell sizes vary, and replicates that empty a cell contribute NaN rows through a >= 50%-finite gate). SEs are replicate SDs (ddof=1); CIs are symmetric normal-approximation `est +/- z_{1-alpha/2} * se`. The closest published validity argument for this style of bootstrap on distributional plug-ins is the exchangeable-bootstrap machinery in Callaway-Li-Oka 2018 (see its review doc). +- **Note (uniform bands at fixed 95%):** `results_.sup_t_crit` ports qte's sup-t critical value (IQR-scaled `sigmahalf` with an SD-floored-at-1e-9 fallback; the 0.95 type-1 quantile of the sup statistics is HARD-CODED in qte independent of `alpha`, and `uniform_bands()` inherits that fixed level - pointwise CIs follow `alpha`, the uniform bands do not). The critical value is computed over ALL grid columns before the interior-range NaN overwrite. +- Point estimates are invariant to `panel`: the estimator uses only the four marginal cell distributions; `panel=True` changes the resampling scheme only (locked by tests). + +*Edge cases:* +- Empty cell -> `ValueError` (Assumption 5.1(ii)). +- NA rows in used columns -> dropped with a counting `UserWarning`. +- Panel mode: duplicate (unit, period) rows -> `ValueError`; group indicator varying within unit -> `ValueError`; units missing a period -> dropped with `UserWarning` (matches qte's `makeBalancedPanel`). +- `unit=` supplied with `panel=False` -> documented-ignore (qte `idname` precedent). +- Requested quantiles at/outside the interior range -> NaN inference, finite points, one warning. +- Bootstrap with < 50% finite replicate rows -> all SEs and `sup_t_crit` NaN + failure-rate warning (`warn_bootstrap_failure_rate`). + +**Deferred (reviewed, documented - see the paper-review docs for full scoping):** +- Covariates (paper Section 5.1 routes; Melly-Santangelo 2015 QR pipeline is the modern reference). `fit()` accepts no covariates; formula covariate terms raise `ValueError`. +- Discrete-outcome bounds and DCIC point identification (Sections 4, 5.2; Kranker's Stata `cic` implements them). +- Analytical SEs (Theorems 5.1-5.3 influence functions; panel Theorems 5.5-5.7; Appendix B covariances). +- Multiple groups/periods (Section 6) - `ecic` (R) is the staggered event-study CiC lineage; Ciaccio's copula-based staggered distributional DiD is a distinct method (reviewed separately, do not conflate). +- Treatment on the controls (Theorem 3.2, group-label exchange). + +**Reference implementation(s):** +- R: `qte::CiC()` (v1.3.1, pinned) - the parity target. Golden fixtures: `benchmarks/R/generate_qte_golden.R` -> `benchmarks/data/qte_golden.json`; parity tests: `tests/test_changes_in_changes_parity.py` (point ATT/QTE at atol=1e-10 across 4 scenarios x panel/RCS, type-1 micro-fixtures at atol=0, seeded-R SE block compared statistically). +- Stata: `cic` (Kranker) - analytical SEs + discrete bounds (deferred scope). + +### QDiD (quantile DiD comparison estimator) + +**Primary source:** Athey & Imbens (2006), Section 3.3 (pp. 446-447) - the paper formalizes QDiD as the natural comparison estimator and recommends CiC over it: QDiD's justifying model (eq. 22) is not invariant to monotone rescaling of the outcome, forces `U ⊥ (G, T)` (identical unobservable distributions in all four cells), and places testable restrictions on the data (footnote 21 - the implementation warns when the implied counterfactual quantile function is non-monotone on the requested grid). + +*Estimator equations (as implemented, matching `qte::QDiD()` v1.3.1 exactly):* + + QTE(tau) = Q7(y11, tau) - [ Q7(y10, tau) + Q7(y01, tau) - Q7(y00, tau) ] + ATT = mean(y11) - ( mean(y10) + mean(Q7(y01, r)) - mean(Q7(y00, r)) ), r = ECDF_y10(y10) + +where `Q7` is the R default type-7 (linear-interpolation) quantile (numpy `method="linear"`). +- **Note (finite-sample deviation from the paper's k^QDID transformation):** the paper's p. 447 display defines QDiD through the transformation `k^QDID(y) = y + F_01^{-1}(F_10(y)) - F_00^{-1}(F_10(y))`; qte 1.3.1 instead computes the additive quantile-DiD above with type-7 quantiles, and its ATT evaluates the control-group quantile functions at the treated pre-period's own-sample ranks. The two constructions are population-equivalent (and QDiD's mean effect equals standard DiD's ATT under continuity, p. 447 - locked as a large-N tolerance test) but are different finite-sample estimators. diff-diff implements the qte form as canonical per the locked parity direction (2026-07-12); the paper-exact transformation form is not implemented. +- No asymptotic theory for QDiD exists in the paper; bootstrap inference (identical machinery to CiC above) is a library/qte convention. No interior-range guard applies (eq. 17 has no QDiD analogue): `q_lower`/`q_upper` are NaN on QDiD results. + +**Reference implementation(s):** +- R: `qte::QDiD()` (v1.3.1, pinned) - same fixture/test infrastructure as CiC. + +--- + # Diagnostics and Sensitivity ## PlaceboTests @@ -4068,6 +4139,8 @@ should be a deliberate user choice. | TripleDifference | Influence function (all methods) | SE = std(IF) / sqrt(n) | | StackedDiD | Cluster-robust (unit) | Cluster at unit × sub-experiment | | TROP | Block bootstrap | — | +| ChangesInChanges | Bootstrap (replicate-SD SEs, normal-approx CIs) | Sup-t uniform bands (fixed 95%, qte parity) | +| QDiD | Bootstrap (replicate-SD SEs, normal-approx CIs) | Sup-t uniform bands (fixed 95%, qte parity) | | BaconDecomposition | N/A (exact decomposition) | Individual 2×2 SEs | | HonestDiD | Inherited from event study | FLCI, C-LF | | PreTrendsPower | Exact (analytical) | - | @@ -4091,6 +4164,8 @@ should be a deliberate user choice. | TripleDifference | triplediff | `ddd()` | | StackedDiD | stacked-did-weights | `create_sub_exp()` + `compute_weights()` | | TROP | - | (forthcoming) | +| ChangesInChanges | qte | `CiC()` | +| QDiD | qte | `QDiD()` | | BaconDecomposition | bacondecomp | `bacon()` | | HonestDiD | HonestDiD | `createSensitivityResults()` | | PreTrendsPower | pretrends | `pretrends()` | diff --git a/docs/practitioner_decision_tree.rst b/docs/practitioner_decision_tree.rst index 5253ea1a5..5dc7765ab 100644 --- a/docs/practitioner_decision_tree.rst +++ b/docs/practitioner_decision_tree.rst @@ -476,10 +476,10 @@ At a Glance What About the Other Estimators? -------------------------------- -diff-diff has 20 estimators covering advanced scenarios: Sun-Abraham for +diff-diff has 22 estimators covering advanced scenarios: Sun-Abraham for interaction-weighted estimation, Imputation DiD and Two-Stage DiD for alternative staggered approaches, Local Projections DiD, Stacked DiD, Efficient DiD, -Triple Difference, TROP, and more. +Triple Difference, TROP, Changes-in-Changes for distributional/quantile effects, and more. The six scenarios above cover the most common business use cases. For the full academic decision tree with all estimators, see :doc:`choosing_estimator`. diff --git a/docs/r_comparison.rst b/docs/r_comparison.rst index fdac4d547..d11f8ac49 100644 --- a/docs/r_comparison.rst +++ b/docs/r_comparison.rst @@ -434,6 +434,8 @@ Feature Comparison Table Stacked DiD requires manual implementation or the ``stackedev`` package; Continuous DiD is available via the ``did`` package continuous extension; Triple Difference requires manual implementation in R. + Changes-in-Changes and QDiD are available via the ``qte`` package + (``qte::CiC()`` / ``qte::QDiD()``, the diff-diff parity target). TROP and Efficient DiD have no direct R equivalents. HeterogeneousAdoptionDiD (dCDH 2026) overlaps with the dedicated R package ``DIDHAD`` (de Chaisemartin et al., 2025), which covers the diff --git a/docs/references.rst b/docs/references.rst index 3d2cbcbba..6fffabfbd 100644 --- a/docs/references.rst +++ b/docs/references.rst @@ -275,6 +275,13 @@ Local Projections DiD Origin of the local-projections method that LP-DiD adapts to the difference-in-differences setting. +Changes-in-Changes / Distributional DiD +--------------------------------------- + +- **Athey, S., & Imbens, G. W. (2006).** "Identification and Inference in Nonlinear Difference-in-Differences Models." *Econometrica*, 74(2), 431-497. https://doi.org/10.1111/j.1468-0262.2006.00668.x + + Primary source for the ``ChangesInChanges`` (alias ``CiC``) and ``QDiD`` estimators: nonlinear DiD recovering the treated group's full counterfactual outcome distribution and quantile treatment effects in the 2x2 design, with the quantile-DiD comparison estimator the paper formalizes alongside it. Point estimation matches the R ``qte`` package (v1.3.1, Callaway) exactly; bootstrap inference follows the same package's conventions. Paper review on file at ``docs/methodology/papers/athey-imbens-2006-review.md``; companion reviews for deferred extensions: ``callaway-li-oka-2018-review.md`` (panel QTT), ``melly-santangelo-2015-review.md`` (covariates), ``ciaccio-2024-review.md`` (staggered). + Continuous Treatment DiD ------------------------ diff --git a/paper.bib b/paper.bib index 0e8f34983..a9dffc07b 100644 --- a/paper.bib +++ b/paper.bib @@ -148,6 +148,17 @@ @misc{Chen2025 doi = {10.48550/arXiv.2506.17729} } +@article{Athey2006, + author = {Athey, Susan and Imbens, Guido W.}, + title = {Identification and Inference in Nonlinear Difference-in-Differences Models}, + journal = {Econometrica}, + volume = {74}, + number = {2}, + pages = {431--497}, + year = {2006}, + doi = {10.1111/j.1468-0262.2006.00668.x} +} + @misc{Athey2025, author = {Athey, Susan and Imbens, Guido W. and Qu, Zhaonan and Viviano, Davide}, title = {Triply Robust Panel Estimators}, diff --git a/paper.md b/paper.md index 95ac021a0..d987a4cb0 100644 --- a/paper.md +++ b/paper.md @@ -21,7 +21,7 @@ bibliography: paper.bib # Summary `diff-diff` is a Python library for Difference-in-Differences (DiD) causal inference -analysis. It provides 20 estimators covering the full modern DiD toolkit - from classic +analysis. It provides 22 estimators covering the full modern DiD toolkit - from classic two-group/two-period designs through heterogeneity-robust staggered adoption methods, synthetic control hybrids, and sensitivity analysis - under a consistent scikit-learn-style API. Most estimators accept an optional `SurveyDesign` object for design-based variance @@ -47,7 +47,7 @@ industry settings for marketing measurement, product experimentation, and policy evaluation - must either context-switch to another language, reimplement methods from scratch, or rely on partial implementations scattered across unrelated packages. -`diff-diff` fills this gap by providing a single-import library that covers 20 estimators +`diff-diff` fills this gap by providing a single-import library that covers 22 estimators with a consistent API, survey-weighted inference, and numerical validation against R. It is also the companion software for the design-based variance framework of @Gerber2026, which establishes design-consistent standard errors for modern DiD estimators under @@ -79,7 +79,7 @@ both, validated against the R reference implementations where they exist. # Key Features -**Breadth of methods.** `diff-diff` implements 20 estimators organized across the modern +**Breadth of methods.** `diff-diff` implements 22 estimators organized across the modern DiD taxonomy. Classic designs include two-group/two-period DiD, two-way fixed effects, and event-study estimation with period-specific effects. Heterogeneity-robust staggered-adoption estimators include Callaway-Sant'Anna [@Callaway2021], Sun-Abraham [@Sun2021], imputation @@ -91,9 +91,10 @@ spatial spillovers [@Butts2021]. Synthetic-control hybrids include synthetic DiD designs include triple-difference and staggered triple-difference estimators [@OrtizVillavicencio2025], continuous-treatment DiD with dose-response curves [@Callaway2024], heterogeneous-adoption designs where no unit remains untreated -[@deChaisemartin2026], nonlinear ETWFE [@Wooldridge2025; @Wooldridge2023], and triply +[@deChaisemartin2026], nonlinear ETWFE [@Wooldridge2025; @Wooldridge2023], distributional effects via +changes-in-changes and quantile DiD [@Athey2006], and triply robust panel estimation [@Athey2025]. Separate diagnostic and sensitivity tools - outside -the 20 estimators - include Goodman-Bacon decomposition [@GoodmanBacon2021], Honest DiD +the 22 estimators - include Goodman-Bacon decomposition [@GoodmanBacon2021], Honest DiD sensitivity analysis [@Rambachan2023], placebo tests, and pre-trends power analysis [@Roth2022]. diff --git a/tests/test_aliases.py b/tests/test_aliases.py index a1eb66213..0f0560bfe 100644 --- a/tests/test_aliases.py +++ b/tests/test_aliases.py @@ -17,6 +17,7 @@ def test_alias_identity(): assert diff_diff.DDD is diff_diff.TripleDifference assert diff_diff.Stacked is diff_diff.StackedDiD assert diff_diff.Bacon is diff_diff.BaconDecomposition + assert diff_diff.CiC is diff_diff.ChangesInChanges def test_aliases_in_all(): @@ -34,6 +35,7 @@ def test_aliases_in_all(): "DDD", "Stacked", "Bacon", + "CiC", ] for alias in aliases: assert alias in diff_diff.__all__, f"{alias} missing from __all__" diff --git a/tests/test_changes_in_changes.py b/tests/test_changes_in_changes.py new file mode 100644 index 000000000..205896ff8 --- /dev/null +++ b/tests/test_changes_in_changes.py @@ -0,0 +1,493 @@ +"""Unit and edge-case tests for ChangesInChanges (CiC) and QDiD. + +Covers constructor validation, the sklearn-style param surface (round-trip, +transactional set_params, clone), fit input validation (formula/kwargs, +panel hygiene, NA handling), the NaN-inference contract with n_bootstrap=0, +bootstrap seeding/failure gating, all diagnostic warnings, and the results +API. Methodology verification lives in test_methodology_changes_in_changes.py +and R parity in test_changes_in_changes_parity.py. +""" + +import warnings + +import numpy as np +import pandas as pd +import pytest + +from diff_diff import ChangesInChanges, ChangesInChangesResults, QDiD, QDiDResults +from tests.conftest import assert_nan_inference + +BOTH = pytest.mark.parametrize("cls", [ChangesInChanges, QDiD], ids=["cic", "qdid"]) + + +def make_2x2(n_treated=60, n_control=80, seed=0, effect=1.0): + """Full-overlap continuous 2x2 panel (long format, one row per unit-period).""" + rng = np.random.default_rng(seed) + n = n_treated + n_control + treat = np.repeat([1, 0], [n_treated, n_control]) + u = rng.normal(0, 1, n) + y_pre = u + rng.normal(0, 0.3, n) + y_post = u + 0.5 + rng.normal(0, 0.3, n) + treat * effect + return pd.DataFrame( + { + "id": np.tile(np.arange(n), 2), + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + + +def fit_quiet(est, df, **kwargs): + """Fit while swallowing the (expected, tested-elsewhere) diagnostic warnings.""" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return est.fit(df, outcome="y", treatment="treated", time="post", **kwargs) + + +# ============================================================================= +# Constructor validation +# ============================================================================= + + +@BOTH +class TestConstructorValidation: + def test_defaults(self, cls): + est = cls() + assert est.quantiles is None + assert est.n_bootstrap == 200 + assert est.alpha == 0.05 + assert est.panel is False + assert est.seed is None + assert est.is_fitted_ is False + assert est.results_ is None + + @pytest.mark.parametrize("bad", [[], [0.0, 0.5], [0.5, 1.0], [0.5, np.nan], "mid"]) + def test_bad_quantiles(self, cls, bad): + with pytest.raises((ValueError, TypeError)): + cls(quantiles=bad) + + @pytest.mark.parametrize("bad", [-1, 2.5, True, "many"]) + def test_bad_n_bootstrap(self, cls, bad): + with pytest.raises(ValueError, match="n_bootstrap"): + cls(n_bootstrap=bad) + + @pytest.mark.parametrize("bad", [0.0, 1.0, 1.5, -0.1, "small", True]) + def test_bad_alpha(self, cls, bad): + with pytest.raises(ValueError, match="alpha"): + cls(alpha=bad) + + @pytest.mark.parametrize("bad", [1, "yes", None]) + def test_bad_panel(self, cls, bad): + with pytest.raises(ValueError, match="panel"): + cls(panel=bad) + + @pytest.mark.parametrize("bad", [-1, 1.5, True, "seed"]) + def test_bad_seed(self, cls, bad): + with pytest.raises(ValueError, match="seed"): + cls(seed=bad) + + def test_error_message_echoes_value(self, cls): + with pytest.raises(ValueError, match="got '-3'"): + cls(n_bootstrap=-3) + + +# ============================================================================= +# get_params / set_params / clone +# ============================================================================= + + +@BOTH +class TestParamSurface: + def test_get_params_round_trips_init(self, cls): + est = cls(quantiles=[0.25, 0.5, 0.75], n_bootstrap=10, alpha=0.1, panel=True, seed=3) + clone = cls(**est.get_params()) + assert clone.get_params() == est.get_params() + + def test_get_params_preserves_raw_none_quantiles(self, cls): + assert cls().get_params()["quantiles"] is None + + def test_set_params_returns_self(self, cls): + est = cls() + assert est.set_params(n_bootstrap=5) is est + assert est.n_bootstrap == 5 + + def test_set_params_unknown_key(self, cls): + with pytest.raises(ValueError, match="Unknown parameter: iters"): + cls().set_params(iters=100) + + def test_set_params_transactional(self, cls): + est = cls(n_bootstrap=100, alpha=0.05) + with pytest.raises(ValueError): + est.set_params(n_bootstrap=50, alpha=2.0) + # The failing batch must not have mutated anything. + assert est.n_bootstrap == 100 + assert est.alpha == 0.05 + + def test_fit_revalidates_after_direct_mutation(self, cls): + est = cls() + est.alpha = 5.0 # bypass set_params + with pytest.raises(ValueError, match="alpha"): + fit_quiet(est, make_2x2()) + + def test_sklearn_clone_if_available(self, cls): + sklearn = pytest.importorskip("sklearn") + est = cls(quantiles=[0.5], n_bootstrap=7, alpha=0.1, panel=True, seed=11) + clone = sklearn.base.clone(est) + assert clone is not est + assert clone.get_params() == est.get_params() + + +# ============================================================================= +# fit() input validation +# ============================================================================= + + +@BOTH +class TestFitValidation: + def test_requires_formula_or_columns(self, cls): + with pytest.raises(ValueError, match="formula"): + cls(n_bootstrap=0).fit(make_2x2(), outcome="y", treatment="treated") + + def test_formula_equals_kwargs(self, cls): + df = make_2x2(seed=5) + r_kw = fit_quiet(cls(n_bootstrap=0), df) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r_f = cls(n_bootstrap=0).fit(df, formula="y ~ treated * post") + r_f2 = cls(n_bootstrap=0).fit(df, formula="y ~ treated + post + treated:post") + assert r_f.att == r_kw.att + assert r_f2.att == r_kw.att + np.testing.assert_array_equal( + r_f.quantile_effects["qte"].to_numpy(), r_kw.quantile_effects["qte"].to_numpy() + ) + + def test_formula_covariates_rejected(self, cls): + with pytest.raises(ValueError, match="[Cc]ovariates"): + cls(n_bootstrap=0).fit(make_2x2(), formula="y ~ treated * post + x1") + + def test_formula_missing_interaction(self, cls): + with pytest.raises(ValueError, match="interaction"): + cls(n_bootstrap=0).fit(make_2x2(), formula="y ~ treated + post") + + def test_missing_column(self, cls): + with pytest.raises(ValueError, match="not found"): + cls(n_bootstrap=0).fit(make_2x2(), outcome="wage", treatment="treated", time="post") + + def test_non_binary_treatment(self, cls): + df = make_2x2() + df.loc[0, "treated"] = 2 + with pytest.raises(ValueError, match="binary"): + fit_quiet(cls(n_bootstrap=0), df) + + def test_non_binary_time(self, cls): + df = make_2x2() + df.loc[0, "post"] = 3 + with pytest.raises(ValueError, match="binary"): + fit_quiet(cls(n_bootstrap=0), df) + + def test_empty_cell_raises(self, cls): + # Remove the entire treated pre-period cell (Assumption 5.1(ii)). + df = make_2x2() + df = df[~((df["treated"] == 1) & (df["post"] == 0))] + with pytest.raises(ValueError, match="Assumption 5.1"): + fit_quiet(cls(n_bootstrap=0), df) + + def test_na_rows_dropped_with_warning(self, cls): + df = make_2x2() + df.loc[:4, "y"] = np.nan + with pytest.warns(UserWarning, match="Dropped 5 row"): + with warnings.catch_warnings(): + warnings.simplefilter("always") + res = cls(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + assert res.n_obs == len(df) - 5 + + +@BOTH +class TestPanelHygiene: + def test_panel_requires_unit(self, cls): + with pytest.raises(ValueError, match="unit"): + fit_quiet(cls(n_bootstrap=0, panel=True), make_2x2()) + + def test_duplicate_unit_period_raises(self, cls): + df = make_2x2() + df = pd.concat([df, df.iloc[[0]]], ignore_index=True) + with pytest.raises(ValueError, match="duplicate"): + fit_quiet(cls(n_bootstrap=0, panel=True), df, unit="id") + + def test_time_varying_group_raises(self, cls): + df = make_2x2() + # Flip one unit's group indicator in the post period only. + idx = df[(df["id"] == 0) & (df["post"] == 1)].index + df.loc[idx, "treated"] = 1 - df.loc[idx, "treated"] + with pytest.raises(ValueError, match="constant within unit"): + fit_quiet(cls(n_bootstrap=0, panel=True), df, unit="id") + + def test_unbalanced_units_dropped_with_warning(self, cls): + df = make_2x2() + df = df.drop(df[(df["id"] == 3) & (df["post"] == 1)].index) + with pytest.warns(UserWarning, match="balanced-panel"): + with warnings.catch_warnings(): + warnings.simplefilter("always") + res = cls(n_bootstrap=0, panel=True).fit( + df, outcome="y", treatment="treated", time="post", unit="id" + ) + assert res.n_obs == len(make_2x2()) - 2 + + def test_unit_ignored_when_rcs(self, cls): + # Documented-ignore: unit= is a no-op with panel=False (qte idname precedent). + df = make_2x2(seed=9) + r1 = fit_quiet(cls(n_bootstrap=0), df, unit="id") + r2 = fit_quiet(cls(n_bootstrap=0), df) + assert r1.att == r2.att + + def test_panel_and_rcs_identical_points(self, cls): + # The point estimator uses only the four marginal cell distributions; + # panel mode changes the bootstrap, never the estimate. + df = make_2x2(seed=11) + r_panel = fit_quiet(cls(n_bootstrap=0, panel=True), df, unit="id") + r_rcs = fit_quiet(cls(n_bootstrap=0), df) + assert r_panel.att == r_rcs.att + np.testing.assert_array_equal( + r_panel.quantile_effects["qte"].to_numpy(), + r_rcs.quantile_effects["qte"].to_numpy(), + ) + + +# ============================================================================= +# Inference contract +# ============================================================================= + + +@BOTH +class TestInferenceContract: + def test_no_bootstrap_nan_inference(self, cls): + res = fit_quiet(cls(n_bootstrap=0), make_2x2()) + assert np.isfinite(res.att) + assert_nan_inference( + { + "se": res.se, + "t_stat": res.t_stat, + "p_value": res.p_value, + "conf_int": res.conf_int, + } + ) + qe = res.quantile_effects + assert np.all(np.isfinite(qe["qte"])) + for _, row in qe.iterrows(): + assert_nan_inference( + { + "se": row["se"], + "t_stat": row["t_stat"], + "p_value": row["p_value"], + "conf_int": (row["conf_low"], row["conf_high"]), + } + ) + assert np.isnan(res.sup_t_crit) + + def test_bootstrap_produces_finite_inference(self, cls): + res = fit_quiet(cls(n_bootstrap=60, seed=1), make_2x2(seed=2)) + assert np.isfinite(res.se) and res.se > 0 + assert np.isfinite(res.p_value) + assert res.conf_int[0] < res.att < res.conf_int[1] + assert np.isfinite(res.sup_t_crit) and res.sup_t_crit > 0 + assert res.n_bootstrap_valid == 60 + + def test_seed_determinism(self, cls): + df = make_2x2(seed=3) + r1 = fit_quiet(cls(n_bootstrap=40, seed=7), df) + r2 = fit_quiet(cls(n_bootstrap=40, seed=7), df) + r3 = fit_quiet(cls(n_bootstrap=40, seed=8), df) + assert r1.se == r2.se + np.testing.assert_array_equal( + r1.quantile_effects["se"].to_numpy(), r2.quantile_effects["se"].to_numpy() + ) + assert r1.se != r3.se + + def test_bootstrap_ci_level_follows_alpha(self, cls): + df = make_2x2(seed=4) + r05 = fit_quiet(cls(n_bootstrap=50, seed=1, alpha=0.05), df) + r20 = fit_quiet(cls(n_bootstrap=50, seed=1, alpha=0.20), df) + assert r05.se == r20.se # same replicates + width05 = r05.conf_int[1] - r05.conf_int[0] + width20 = r20.conf_int[1] - r20.conf_int[0] + assert width20 < width05 + + +class TestCiCInteriorRangeGuard: + def test_exterior_quantiles_keep_points_nan_inference(self): + # Treated-pre support wider than control-pre: eq. 17 interior shrinks. + rng = np.random.default_rng(0) + n = 120 + df = pd.DataFrame( + { + "id": np.tile(np.arange(n), 2), + "post": np.repeat([0, 1], n), + "treated": np.tile(np.repeat([1, 0], [60, 60]), 2), + } + ) + y_pre = np.where(df["treated"][:n] == 1, rng.normal(0, 3, n), rng.normal(0, 1, n)) + y_post = y_pre + 0.5 + rng.normal(0, 0.2, n) + df["y"] = np.concatenate([y_pre, y_post]) + + est = ChangesInChanges(n_bootstrap=30, seed=5) + with pytest.warns(UserWarning, match="interior range"): + with warnings.catch_warnings(): + warnings.simplefilter("always") + res = est.fit(df, outcome="y", treatment="treated", time="post") + qe = res.quantile_effects + exterior = ~((qe["quantile"] > res.q_lower) & (qe["quantile"] < res.q_upper)) + assert exterior.any(), "test DGP must produce exterior quantiles" + # Points survive (qte parity), inference is NaN outside the interior. + assert np.all(np.isfinite(qe["qte"])) + assert qe.loc[exterior, "se"].isna().all() + assert qe.loc[exterior, "p_value"].isna().all() + assert qe.loc[~exterior, "se"].notna().all() + # sup_t_crit is computed over ALL columns before the overwrite. + assert np.isfinite(res.sup_t_crit) + + def test_qdid_has_no_guard(self): + res = fit_quiet(QDiD(n_bootstrap=0), make_2x2()) + assert np.isnan(res.q_lower) and np.isnan(res.q_upper) + + +# ============================================================================= +# Diagnostic warnings +# ============================================================================= + + +class TestWarnings: + def test_support_violation_warns_cic(self): + df = make_2x2(seed=1) + df.loc[(df["treated"] == 1) & (df["post"] == 0), "y"] += 10 # shift out of support + with pytest.warns(UserWarning, match="support"): + ChangesInChanges(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + + @BOTH + def test_ties_warn(self, cls): + df = make_2x2(seed=2) + df["y"] = np.round(df["y"]) # heavy ties + with pytest.warns(UserWarning, match="ties"): + cls(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + + def test_qdid_non_monotone_warns(self): + # Crossing quantile curves: control shrinks spread strongly over time + # while the treated-pre distribution is wide. + rng = np.random.default_rng(3) + n = 200 + treat = np.repeat([1, 0], n // 2) + y_pre = np.where(treat == 1, rng.normal(0, 4, n), rng.normal(0, 3, n)) + y_post = np.where(treat == 1, rng.normal(1, 4, n), rng.normal(0.5, 0.1, n)) + df = pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + with pytest.warns(UserWarning, match="non-monotone"): + QDiD(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + + def test_clean_data_no_unexpected_warnings(self): + df = make_2x2(n_treated=150, n_control=150, seed=12) + # Deterministically shrink the treated pre-period sample strictly inside + # the control pre-period support so the (legitimate, tested-above) + # support warning cannot fire on this random draw. + pre_t = (df["treated"] == 1) & (df["post"] == 0) + pre_c = (df["treated"] == 0) & (df["post"] == 0) + lo, hi = df.loc[pre_c, "y"].min(), df.loc[pre_c, "y"].max() + center = 0.5 * (lo + hi) + y10 = df.loc[pre_t, "y"] + scale = 0.4 * (hi - lo) / (y10.max() - y10.min()) + df.loc[pre_t, "y"] = center + (y10 - y10.mean()) * scale + with warnings.catch_warnings(): + warnings.simplefilter("error") + ChangesInChanges(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + + +class TestBootstrapFailureGate: + def test_tiny_cells_gate_to_nan(self): + # 2 obs per cell: pooled RCS resampling frequently empties a cell. + rng = np.random.default_rng(0) + df = pd.DataFrame( + { + "post": [0, 0, 1, 1] * 2, + "treated": [0] * 4 + [1] * 4, + "y": rng.normal(0, 1, 8), + } + ) + with warnings.catch_warnings(record=True) as rec: + warnings.simplefilter("always") + res = ChangesInChanges(n_bootstrap=200, seed=0).fit( + df, outcome="y", treatment="treated", time="post" + ) + messages = [str(w.message) for w in rec] + assert any("bootstrap iterations succeeded" in m for m in messages) + assert res.n_bootstrap_valid < 200 + assert np.isfinite(res.att) # point estimate unaffected + + +# ============================================================================= +# Results API +# ============================================================================= + + +@BOTH +class TestResultsAPI: + def test_results_types_and_fields(self, cls): + res = fit_quiet(cls(n_bootstrap=25, seed=1), make_2x2()) + assert isinstance(res, ChangesInChangesResults) + assert res.estimator == ("cic" if cls is ChangesInChanges else "qdid") + assert res.cell_sizes == { + "control_pre": 80, + "control_post": 80, + "treated_pre": 60, + "treated_post": 60, + } + assert len(res.quantile_effects) == 19 # default grid + + def test_summary_smoke(self, cls): + res = fit_quiet(cls(n_bootstrap=25, seed=1), make_2x2()) + text = res.summary() + assert "ATT" in text + assert "Quantile treatment effects" in text + + def test_to_dict_keys(self, cls): + d = fit_quiet(cls(n_bootstrap=0), make_2x2()).to_dict() + for key in ("att", "se", "conf_int_lower", "conf_int_upper", "estimator", "panel"): + assert key in d + assert d["inference_method"] == "none" + + def test_to_dataframe_levels(self, cls): + res = fit_quiet(cls(n_bootstrap=0), make_2x2()) + assert len(res.to_dataframe("quantiles")) == 19 + assert len(res.to_dataframe("att")) == 1 + with pytest.raises(ValueError, match="level"): + res.to_dataframe("horizons") + + def test_uniform_bands(self, cls): + res = fit_quiet(cls(n_bootstrap=40, seed=2), make_2x2()) + bands = res.uniform_bands() + finite = res.quantile_effects["se"].notna() + # Sup-t bands are at least as wide as pointwise CIs on finite rows. + assert np.all( + bands.loc[finite, "band_low"].to_numpy() + <= res.quantile_effects.loc[finite, "conf_low"].to_numpy() + 1e-12 + ) + + def test_fitted_state(self, cls): + est = cls(n_bootstrap=0) + assert not est.is_fitted_ + res = fit_quiet(est, make_2x2()) + assert est.is_fitted_ + assert est.results_ is res + + def test_custom_quantile_grid(self, cls): + res = fit_quiet(cls(quantiles=[0.25, 0.5, 0.75], n_bootstrap=0), make_2x2()) + np.testing.assert_array_equal( + res.quantile_effects["quantile"].to_numpy(), [0.25, 0.5, 0.75] + ) + + +def test_qdid_results_alias(): + assert QDiDResults is ChangesInChangesResults diff --git a/tests/test_changes_in_changes_parity.py b/tests/test_changes_in_changes_parity.py new file mode 100644 index 000000000..4fe690c5b --- /dev/null +++ b/tests/test_changes_in_changes_parity.py @@ -0,0 +1,201 @@ +"""R-parity golden-fixture tests for ChangesInChanges (CiC) and QDiD vs qte 1.3.1. + +Fixture: benchmarks/data/qte_golden.json, generated by +benchmarks/R/generate_qte_golden.R (qte pinned to 1.3.1 there and in +benchmarks/R/requirements.R). Point fixtures were generated with se=FALSE and +are deterministic; the se_block was generated seeded (set.seed(42), iters=999) +and is compared statistically since bootstrap draws cannot be replicated +across languages. + +Tolerances: +- POINT_ATOL = 1e-10 for end-to-end ATT/QTE values (means and type-7 + interpolation differ from R by ~1 ulp; type-1 selections are bit-exact). +- Micro-fixtures: type-1 quantiles at atol=0 (pure order-statistic selection, + bit-exact cross-language); type-7 at rtol=1e-15 (R's (1-h)*lo + h*hi vs + numpy's lerp differ in the last ulp). +- SE parity: threshold = 0.40 if n_boot < 100 else 0.15 for the ATT SE and + 0.25 per-quantile (tail-quantile bootstrap SEs are noisier at any replicate + count; both sides estimate the same scheme-identical bootstrap SD). +""" + +import json +import warnings +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from diff_diff import ChangesInChanges, QDiD +from diff_diff.changes_in_changes import _quantile_type1, _quantile_type7 + +FIXTURE_PATH = Path(__file__).parent.parent / "benchmarks" / "data" / "qte_golden.json" + +POINT_ATOL = 1e-10 +QTE_VERSION = "1.3.1" + + +def _load_fixture(): + if not FIXTURE_PATH.exists(): + pytest.skip( + f"Golden fixture {FIXTURE_PATH} missing - regenerate via " + f"`Rscript benchmarks/R/generate_qte_golden.R`." + ) + with open(FIXTURE_PATH) as f: + return json.load(f) + + +@pytest.fixture(scope="module") +def fixture(): + return _load_fixture() + + +@pytest.fixture(scope="module") +def probs(fixture): + # Use the R-stored probs doubles: R's seq() and np.arange() can differ in + # trailing ulps, and the quantile index arithmetic is sensitive to them. + return np.asarray(fixture["metadata"]["probs"], dtype=float) + + +def _scenario_df(scenario): + return pd.DataFrame({k: scenario["data"][k] for k in ("id", "period", "treat", "y")}) + + +def _fit(df, method, mode, probs, n_bootstrap=0, seed=None): + cls = ChangesInChanges if method == "cic" else QDiD + est = cls( + quantiles=probs, + n_bootstrap=n_bootstrap, + panel=(mode == "panel"), + seed=seed, + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return est.fit( + df, + outcome="y", + treatment="treat", + time="period", + unit="id" if mode == "panel" else None, + ) + + +def test_metadata_versions_match(fixture): + """The committed fixture must have been generated against the pinned qte.""" + assert fixture["metadata"]["qte_version"] == QTE_VERSION + + +SCENARIOS = ["normal_2x2_n500", "lognormal_2x2_n300", "smalln_2x2_n60", "lalonde_psid"] +RESULT_KEYS = ["cic_panel", "cic_rcs", "qdid_panel", "qdid_rcs"] + + +@pytest.mark.parametrize("scenario_name", SCENARIOS) +@pytest.mark.parametrize("result_key", RESULT_KEYS) +def test_point_parity(fixture, probs, scenario_name, result_key): + scenario = fixture["scenarios"].get(scenario_name) + if scenario is None: + pytest.skip(f"Scenario {scenario_name} missing from fixture") + golden = scenario["results"][result_key] + method, mode = result_key.rsplit("_", 1) + + res = _fit(_scenario_df(scenario), method, mode, probs) + + np.testing.assert_allclose(res.att, golden["ate"], atol=POINT_ATOL, rtol=0) + np.testing.assert_allclose( + res.quantile_effects["qte"].to_numpy(), + np.asarray(golden["qte"], dtype=float), + atol=POINT_ATOL, + rtol=0, + ) + + +def test_lalonde_ties_warning(fixture, probs): + """lalonde re78 zeros trigger the discreteness warning; points still match.""" + scenario = fixture["scenarios"].get("lalonde_psid") + if scenario is None: + pytest.skip("lalonde_psid missing from fixture") + df = _scenario_df(scenario) + est = ChangesInChanges(quantiles=probs, n_bootstrap=0) + with pytest.warns(UserWarning, match="ties"): + res = est.fit(df, outcome="y", treatment="treat", time="period") + np.testing.assert_allclose( + res.att, scenario["results"]["cic_rcs"]["ate"], atol=POINT_ATOL, rtol=0 + ) + + +class TestQuantileMicroFixtures: + """Raw R quantile outputs on adversarial probability grids.""" + + def test_type1_bit_exact(self, fixture): + for name, case in fixture["quantile_cases"].items(): + x = np.sort(np.asarray(case["x"], dtype=float)) + p = np.asarray(case["probs"], dtype=float) + np.testing.assert_array_equal( + _quantile_type1(x, p), + np.asarray(case["type1"], dtype=float), + err_msg=f"type-1 mismatch in micro-fixture '{name}'", + ) + + def test_type7_last_ulp(self, fixture): + for name, case in fixture["quantile_cases"].items(): + x = np.sort(np.asarray(case["x"], dtype=float)) + p = np.asarray(case["probs"], dtype=float) + np.testing.assert_allclose( + _quantile_type7(x, p), + np.asarray(case["type7"], dtype=float), + rtol=1e-15, + atol=1e-300, + err_msg=f"type-7 mismatch in micro-fixture '{name}'", + ) + + +class TestSEStatisticalParity: + """Scheme-identical bootstrap SEs agree statistically (never bit-exactly: + qte's bootstrap is unseeded through its public API, so the R draws cannot + be replicated; both sides estimate the SD of the same resampling scheme).""" + + @pytest.mark.parametrize("block_key", ["cic_panel", "cic_rcs", "qdid_panel"]) + def test_se_parity(self, fixture, probs, ci_params, block_key): + block = fixture["se_block"].get(block_key) + if block is None: + pytest.skip(f"se_block {block_key} missing from fixture") + scenario = fixture["scenarios"]["normal_2x2_n500"] + method, mode = block_key.rsplit("_", 1) + + n_boot = ci_params.bootstrap(499, min_n=199) + res = _fit(_scenario_df(scenario), method, mode, probs, n_bootstrap=n_boot, seed=42) + + # ATT SE: repo convention threshold. + att_threshold = 0.40 if n_boot < 100 else 0.15 + r_ate_se = float(block["ate_se"]) + rel = abs(res.se - r_ate_se) / r_ate_se + assert rel < att_threshold, ( + f"{block_key}: ATT SE rel diff {rel:.3f} vs R " + f"({res.se:.5f} vs {r_ate_se:.5f}, n_boot={n_boot})" + ) + + # Per-quantile SEs: looser bound - tail-quantile bootstrap SEs are + # noisier than the ATT SE at any replicate count. + qte_threshold = 0.40 if n_boot < 100 else 0.25 + r_qte_se = np.asarray(block["qte_se"], dtype=float) + py_qte_se = res.quantile_effects["se"].to_numpy() + rel_q = np.abs(py_qte_se - r_qte_se) / r_qte_se + assert np.nanmax(rel_q) < qte_threshold, ( + f"{block_key}: worst per-quantile SE rel diff {np.nanmax(rel_q):.3f} " + f"(n_boot={n_boot})" + ) + + def test_sup_t_crit_loose_parity(self, fixture, probs, ci_params): + """The sup-t critical value is a bootstrap quantile - compare loosely.""" + block = fixture["se_block"].get("cic_panel") + if block is None: + pytest.skip("se_block cic_panel missing from fixture") + scenario = fixture["scenarios"]["normal_2x2_n500"] + n_boot = ci_params.bootstrap(499, min_n=199) + res = _fit(_scenario_df(scenario), "cic", "panel", probs, n_bootstrap=n_boot, seed=42) + r_c = float(block["sup_t_crit"]) + assert np.isfinite(res.sup_t_crit) + rel = abs(res.sup_t_crit - r_c) / r_c + assert ( + rel < 0.40 + ), f"sup-t critical value rel diff {rel:.3f} vs R ({res.sup_t_crit:.3f} vs {r_c:.3f})" diff --git a/tests/test_methodology_changes_in_changes.py b/tests/test_methodology_changes_in_changes.py new file mode 100644 index 000000000..9d4a72f03 --- /dev/null +++ b/tests/test_methodology_changes_in_changes.py @@ -0,0 +1,293 @@ +"""Methodology verification for ChangesInChanges (CiC) and QDiD. + +Verifies the implementation against: +1. The canonical formulas of Athey & Imbens (2006), Econometrica 74(2), + as documented in docs/methodology/papers/athey-imbens-2006-review.md and + docs/methodology/REGISTRY.md (eqs. 34-36, 17-18, A.1-A.2; Lemma A.1). +2. Population-level relations the paper establishes: QDiD's mean effect + coincides with standard DiD's ATT under continuity (p. 447); CiC and DiD + probability limits coincide under the nested additive-linear model + (p. 463); CiC is equivariant to monotone transformations of the outcome. +3. Hand-calculated micro-examples for every estimator formula. + +R-package parity lives in test_changes_in_changes_parity.py. +""" + +import warnings + +import numpy as np +import pandas as pd +import pytest + +from diff_diff import ChangesInChanges, DifferenceInDifferences, QDiD +from diff_diff.changes_in_changes import ( + _cic_point, + _ecdf, + _interior_range, + _qdid_point, + _quantile_type1, + _split_cells, +) + + +def fit_quiet(est, df, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return est.fit(df, outcome="y", treatment="treated", time="post", **kwargs) + + +# ============================================================================= +# Eq. (34)-(35)/(A.1)-(A.2) inverse-CDF conventions +# ============================================================================= + + +class TestInverseCDFConventions: + """The eq. (A.2) sandwich identities and Lemma A.1 Galois identities hold + for the (_ecdf, _quantile_type1) pair - the load-bearing property of the + paper's asymptotic theory, violated by interpolating quantile definitions + (e.g. numpy's default 'linear'). + + Exactness caveat: the identities are exact-arithmetic statements. In + floating point they hold bit-exactly whenever the ECDF values k/n are + binary-exact (n a power of two); for other n the float product n*(k/n) + can land one ulp above k and shift the selection by one order statistic - + R's quantile.default behaves identically (pinned by the bit-exact type-1 + micro-fixtures in test_changes_in_changes_parity.py), so the estimator + still matches R exactly. Power-of-two sizes test exactness; a non-dyadic + size tests the 1/n sandwich bound. + """ + + @pytest.fixture(params=[8, 16, 64, 256]) + def sample(self, request): + rng = np.random.default_rng(request.param) + return np.sort(rng.normal(0, 1, request.param)) + + def test_sandwich_upper(self, sample): + # q <= F(F^{-1}(q)) < q + 1/N, equality iff q = j/N. + n = sample.size + q = np.linspace(0.001, 0.999, 97) + fq = _ecdf(sample, _quantile_type1(sample, q)) + assert np.all(fq >= q) + assert np.all(fq < q + 1.0 / n + 1e-15) + j_over_n = np.arange(0, n + 1) / n + fq_exact = _ecdf(sample, _quantile_type1(sample, j_over_n[1:])) + np.testing.assert_array_equal(fq_exact, j_over_n[1:]) + + def test_sandwich_upper_non_dyadic(self): + # Non-power-of-two n: the sandwich bound still holds within 1/n slack + # on both sides (one-order-statistic float slippage, identical to R). + rng = np.random.default_rng(53) + sample = np.sort(rng.normal(0, 1, 53)) + n = sample.size + q = np.linspace(0.001, 0.999, 197) + fq = _ecdf(sample, _quantile_type1(sample, q)) + assert np.all(fq >= q - 1e-15) + assert np.all(fq < q + 2.0 / n) + + def test_sandwich_lower_at_sample_points(self, sample): + # F^{-1}(F(y)) == y exactly at all sample values (eq. A.2). + np.testing.assert_array_equal(_quantile_type1(sample, _ecdf(sample, sample)), sample) + + def test_inverse_at_zero_is_minimum(self, sample): + # eq. (35): F^{-1}(0) = sample minimum, never -inf. + assert _quantile_type1(sample, np.array([0.0]))[0] == sample[0] + + def test_inverse_at_one_is_maximum(self, sample): + assert _quantile_type1(sample, np.array([1.0]))[0] == sample[-1] + + def test_galois_idempotence(self, sample): + # Lemma A.1 (iii)/(iv): g(g^{-1}(g(y))) = g(y); g^{-1}(g(g^{-1}(u))) = g^{-1}(u). + # The identities are stated on the support Y (Lemma A.1's domain): below + # the sample minimum, g(y) = 0 while g^{-1}(0) is the minimum under the + # eq. (35) convention, so (iii) intentionally does not extend there. + y = np.linspace(sample[0], sample[-1] + 1, 41) + gy = _ecdf(sample, y) + np.testing.assert_array_equal(_ecdf(sample, _quantile_type1(sample, gy)), gy) + u = np.linspace(0.01, 0.99, 37) + ginv_u = _quantile_type1(sample, u) + np.testing.assert_array_equal(_quantile_type1(sample, _ecdf(sample, ginv_u)), ginv_u) + + +# ============================================================================= +# Hand-calculated micro-example +# ============================================================================= + + +class TestHandCalculated: + """4-cell micro-example small enough to verify every number by hand. + + y00 = [1, 2, 3, 4] (control pre) + y01 = [2, 4, 6, 8] (control post: doubling transformation h(u,1) = 2u) + y10 = [2, 3] (treated pre) + y11 = [10, 12] (treated post) + + ECDF ranks of y10 in y00: F00(2) = 2/4, F00(3) = 3/4. + Counterfactual draws: Q1(y01, 0.50) = y01_(2) = 4; Q1(y01, 0.75) = y01_(3) = 6. + CiC ATT = mean(10, 12) - mean(4, 6) = 11 - 5 = 6. + """ + + def setup_method(self): + y = np.array([1, 2, 3, 4, 2, 4, 6, 8, 2, 3, 10, 12], dtype=float) + g = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]) + t = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1]) + self.cells = _split_cells(y, g, t) + + def test_cic_att(self): + att, _, cf = _cic_point(self.cells, np.array([0.5])) + np.testing.assert_array_equal(cf, [4.0, 6.0]) + assert att == 6.0 + + def test_cic_qte(self): + # Q1(y11, 0.5) = 10 (first order stat, ceil(2*0.5)=1); Q1(cf, 0.5) = 4. + _, qte, _ = _cic_point(self.cells, np.array([0.5])) + assert qte[0] == 10.0 - 4.0 + + def test_interior_range(self): + # q_lower = F10(min y00) = F10(1) = 0; q_upper = F10(max y00) = F10(4) = 1. + q_lower, q_upper = _interior_range(self.cells) + assert q_lower == 0.0 + assert q_upper == 1.0 + + def test_qdid_att(self): + # Own-sample ranks of y10: F10(2) = 1/2, F10(3) = 1. + # Type-7 quantiles: Q7(y01, [.5, 1]) = [5, 8]; Q7(y00, [.5, 1]) = [2.5, 4]. + # ATT = 11 - (2.5 + mean(5, 8) - mean(2.5, 4)) = 11 - (2.5 + 6.5 - 3.25) = 5.25. + att, _ = _qdid_point(self.cells, np.array([0.5])) + assert att == 11.0 - (2.5 + 6.5 - 3.25) + + def test_qdid_qte(self): + # Q7 at 0.5: y11 -> 11, y10 -> 2.5, y01 -> 5, y00 -> 2.5. + # qte(.5) = 11 - (2.5 + 5 - 2.5) = 6. + _, qte = _qdid_point(self.cells, np.array([0.5])) + assert qte[0] == 6.0 + + +# ============================================================================= +# Population-level relations from the paper +# ============================================================================= + + +def make_additive_panel(n, seed, effect=1.5): + """Additive-linear DGP: the standard DiD model (eqs. 1, 4-6), where CiC, + QDiD, and DiD all identify the same ATT.""" + rng = np.random.default_rng(seed) + treat = np.repeat([1, 0], [n // 2, n - n // 2]) + eps_pre = rng.normal(0, 1, n) + eps_post = rng.normal(0, 1, n) + gamma = 0.7 * treat + y_pre = 0.2 + gamma + eps_pre + y_post = 0.2 + 0.6 + gamma + eps_post + treat * effect + return pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + + +class TestPopulationRelations: + def test_qdid_att_matches_did_at_large_n(self): + # p. 447: E[k^DID(Y_10)] = E[k^QDID(Y_10)] under continuity - QDiD's + # mean effect is standard DiD's ATT in population. Finite-sample + # deviation comes from qte's rank-formula ATT (REGISTRY Note), so the + # comparison uses a large sample and a loose tolerance. + df = make_additive_panel(20000, seed=42) + qdid = fit_quiet(QDiD(n_bootstrap=0), df) + did = DifferenceInDifferences().fit(df, outcome="y", treatment="treated", time="post") + assert qdid.att == pytest.approx(did.att, abs=0.05) + + def test_cic_matches_did_on_additive_dgp(self): + # p. 463: under the nested linear model with full independence the CiC + # and DiD probability limits coincide. + df = make_additive_panel(20000, seed=7) + cic = fit_quiet(ChangesInChanges(n_bootstrap=0), df) + did = DifferenceInDifferences().fit(df, outcome="y", treatment="treated", time="post") + assert cic.att == pytest.approx(did.att, abs=0.05) + + def test_cic_scale_invariance_nonlinear_dgp(self): + # CiC's assumptions are invariant to monotone transformations (p. 437): + # on a multiplicative DGP, CiC estimated in logs then exponentiated + # differs from DiD in levels, but CiC handles both consistently. Here + # we check the sharper finite-sample property: monotone-transform + # equivariance of the counterfactual draws (interpolation-free + # pipeline), via quantile effects of exp(y). + rng = np.random.default_rng(3) + n = 400 + treat = np.repeat([1, 0], n // 2) + u = rng.normal(0, 0.5, n) + y_pre = u + rng.normal(0, 0.1, n) + y_post = 1.4 * u + 0.3 + rng.normal(0, 0.1, n) + treat * 0.5 + df = pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + grid = np.array([0.2, 0.5, 0.8]) + + res_log = fit_quiet(ChangesInChanges(quantiles=grid, n_bootstrap=0), df) + df_exp = df.assign(y=np.exp(df["y"])) + res_exp = fit_quiet(ChangesInChanges(quantiles=grid, n_bootstrap=0), df_exp) + + # Equivariance: counterfactual and observed quantiles commute with exp + # EXACTLY (both are order statistics / selections, never interpolated), + # so exp-scale QTEs equal the exp of the log-scale quantile endpoints. + cells = _split_cells( + df["y"].to_numpy(), + df["treated"].to_numpy(), + df["post"].to_numpy(), + ) + _, _, cf_log = _cic_point(cells, grid) + q11_log = _quantile_type1(np.sort(cells["y11"]), grid) + qcf_log = _quantile_type1(cf_log, grid) + expected_exp_qte = np.exp(q11_log) - np.exp(qcf_log) + np.testing.assert_array_equal(res_exp.quantile_effects["qte"].to_numpy(), expected_exp_qte) + # And the log-scale run is internally consistent with the same endpoints. + np.testing.assert_array_equal(res_log.quantile_effects["qte"].to_numpy(), q11_log - qcf_log) + + def test_cic_recovers_heterogeneous_quantile_effects(self): + # Monotone-in-u heterogeneous effects: the QTE curve should be + # increasing across quantiles and bracket the true effect range. + rng = np.random.default_rng(11) + n = 30000 + treat = np.repeat([1, 0], n // 2) + u = rng.normal(0, 1, n) + y_pre = u + rng.normal(0, 0.05, n) + effect = 0.5 + 0.5 * (u > 0) # 0.5 below median-u, 1.0 above + y_post = u + 0.3 + rng.normal(0, 0.05, n) + treat * effect + df = pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + res = fit_quiet(ChangesInChanges(quantiles=np.array([0.1, 0.9]), n_bootstrap=0), df) + qte = res.quantile_effects["qte"].to_numpy() + assert qte[0] == pytest.approx(0.5, abs=0.1) + assert qte[1] == pytest.approx(1.0, abs=0.1) + + +# ============================================================================= +# Interior range hand-check on constructed data +# ============================================================================= + + +def test_interior_range_construction(): + # y00 spans [0, 1]; y10 has exactly 25 of 100 points below 0 and 25 above 1: + # q_lower = F10(min y00) = F10(0) = 0.25, q_upper = F10(max y00) = F10(1) = 0.75. + y00 = np.linspace(0, 1, 50) + y10 = np.concatenate( + [np.linspace(-1, -0.01, 25), np.linspace(0.01, 0.99, 50), np.linspace(1.01, 2, 25)] + ) + y01 = np.linspace(0, 1, 50) + y11 = np.linspace(0, 1, 50) + y = np.concatenate([y00, y01, y10, y11]) + g = np.concatenate([np.zeros(100), np.ones(150)]) + t = np.concatenate([np.zeros(50), np.ones(50), np.zeros(100), np.ones(50)]) + cells = _split_cells(y, g, t) + q_lower, q_upper = _interior_range(cells) + assert q_lower == 0.25 + assert q_upper == 0.75 From 965142626ca17f754187f01439804191400bd24e Mon Sep 17 00:00:00 2001 From: igerber Date: Sun, 12 Jul 2026 15:39:18 -0400 Subject: [PATCH 2/3] fix: harden CiC/QDiD inference contract, default grid, and input validation - NaN-ify invalid per-quantile SEs (non-finite or <= 0, e.g. zero-spread degenerate bootstrap columns) before storing quantile_effects, so the stored se honors the joint-NaN contract and uniform_bands() cannot emit finite zero-width bands while pointwise inference is NaN; degenerate constant-cell regression test - Pin _DEFAULT_QUANTILES to R's exact seq(0.05, 0.95, 0.05) doubles (np.arange differs at 5 of 19 indices by one ulp; type-1 order-statistic selection is ulp-sensitive); locked against the fixture probs plus a quantiles=None end-to-end golden parity test - Reject non-finite (inf/-inf) outcome values with a counting ValueError after the NaN drop (dropna keeps inf); +/-inf tests - get_params(deep=True) sklearn-clone-compatible signature on both classes (HAD precedent) + deep-flag equality and strengthened clone tests - Explicit-interaction formulas derive (treatment, time) roles from the main-effect order so 'treated:post' and 'post:treated' are identical (CiC/QDiD are not symmetric in treatment/time); order-invariance test - Add the qdid_rcs seeded SE block to the golden generator and SE parity parametrization (fixture regenerated; point values unchanged) Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_011hismLBFcUbUzvDRq8ruWb --- benchmarks/R/generate_qte_golden.R | 3 +- benchmarks/data/qte_golden.json | 2 +- diff_diff/changes_in_changes.py | 70 ++++++++++++++++++++++--- tests/test_changes_in_changes.py | 60 ++++++++++++++++++++- tests/test_changes_in_changes_parity.py | 33 +++++++++++- 5 files changed, 155 insertions(+), 13 deletions(-) diff --git a/benchmarks/R/generate_qte_golden.R b/benchmarks/R/generate_qte_golden.R index 08ac2cab9..d251ef64f 100644 --- a/benchmarks/R/generate_qte_golden.R +++ b/benchmarks/R/generate_qte_golden.R @@ -150,7 +150,8 @@ run_se <- function(df, method, panel, seed) { se_block <- list( cic_panel = run_se(dgps$normal_2x2_n500, "cic", TRUE, seed = 42), cic_rcs = run_se(dgps$normal_2x2_n500, "cic", FALSE, seed = 42), - qdid_panel = run_se(dgps$normal_2x2_n500, "qdid", TRUE, seed = 42) + qdid_panel = run_se(dgps$normal_2x2_n500, "qdid", TRUE, seed = 42), + qdid_rcs = run_se(dgps$normal_2x2_n500, "qdid", FALSE, seed = 42) ) message("SE block done.") diff --git a/benchmarks/data/qte_golden.json b/benchmarks/data/qte_golden.json index 89e0f3e31..238387e3d 100644 --- a/benchmarks/data/qte_golden.json +++ b/benchmarks/data/qte_golden.json @@ -1 +1 @@ -{"metadata":{"description":"Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte. Point fixtures use se=FALSE (deterministic); the se_block is seeded (set.seed before each call, iters=999) and compared 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diff --git a/diff_diff/changes_in_changes.py b/diff_diff/changes_in_changes.py index 666eb9d2f..fe53e1f83 100644 --- a/diff_diff/changes_in_changes.py +++ b/diff_diff/changes_in_changes.py @@ -46,8 +46,34 @@ from diff_diff.changes_in_changes_results import ChangesInChangesResults from diff_diff.utils import safe_inference, safe_inference_batch, validate_binary -# Default quantile grid: matches qte's ``probs = seq(0.05, 0.95, 0.05)`` (19 points). -_DEFAULT_QUANTILES = np.arange(0.05, 0.96, 0.05) +# Default quantile grid: qte's ``probs = seq(0.05, 0.95, 0.05)`` (19 points), pinned to +# R's EXACT seq() doubles. ``np.arange(0.05, 0.96, 0.05)`` differs from R at 5 of the 19 +# indices by one ulp, and type-1 order-statistic selection is sensitive to those ulps on +# n*p integer boundaries - so the default is hardcoded rather than computed. Locked +# against the golden fixture's stored probs by tests/test_changes_in_changes_parity.py. +_DEFAULT_QUANTILES = np.array( + [ + 0.05, + 0.1, + 0.15000000000000002, + 0.2, + 0.25, + 0.3, + 0.35000000000000003, + 0.4, + 0.45, + 0.5, + 0.55, + 0.6000000000000001, + 0.6500000000000001, + 0.7000000000000001, + 0.7500000000000001, + 0.8, + 0.8500000000000001, + 0.9000000000000001, + 0.95, + ] +) # Duplicate-share threshold above which the discrete-outcome warning fires. Library # choice: the paper's continuous machinery (Assumption 5.1(iii)) has no finite-sample @@ -234,12 +260,15 @@ def _parse_2x2_formula(formula: str, data: pd.DataFrame) -> Tuple[str, str, str] pair = [p.strip() for p in interaction.split(":")] if len(pair) != 2: raise ValueError("Interaction term must involve exactly two variables") - treatment, time = pair if sorted(mains) != sorted(pair): raise ValueError( "Covariates are not supported by ChangesInChanges/QDiD (deferred from v1; " "the formula must be 'outcome ~ treatment + time + treatment:time')." ) + # Roles come from the MAIN-EFFECT order, not the interaction-term order: + # CiC/QDiD are not symmetric in (treatment, time), and 'treated:post' vs + # 'post:treated' must not silently swap semantics. + treatment, time = mains[0], mains[1] else: raise ValueError( "Formula must include an interaction term (treatment * time or treatment:time)" @@ -466,6 +495,15 @@ def _fit_distributional( if len(frame) == 0: raise ValueError("No observations remain after dropping missing values") + y_check = frame[outcome].to_numpy(dtype=float) + if not np.all(np.isfinite(y_check)): + n_nonfinite = int(np.count_nonzero(~np.isfinite(y_check))) + raise ValueError( + f"Outcome column '{outcome}' contains {n_nonfinite} non-finite value(s) " + "(inf/-inf). Clean or drop these observations before fitting - they would " + "silently corrupt the empirical CDFs, quantiles, and bootstrap." + ) + validate_binary(frame[treatment].to_numpy(dtype=float), "treatment") validate_binary(frame[time].to_numpy(dtype=float), "time") @@ -542,6 +580,12 @@ def _fit_distributional( sup_t_crit = np.nan # ---- inference assembly --------------------------------------------------- + # Joint-NaN contract: an invalid SE (non-finite or <= 0, e.g. a degenerate + # bootstrap column with zero spread) must NaN the STORED se too, not only the + # t/p/CI that safe_inference_batch masks - otherwise uniform_bands() would + # build finite zero-width bands from a 0.0 se while pointwise inference is NaN. + att_se = att_se if (np.isfinite(att_se) and att_se > 0) else np.nan + qte_ses = np.where(np.isfinite(qte_ses) & (qte_ses > 0), qte_ses, np.nan) t_stat, p_value, conf_int = safe_inference(att, att_se, est.alpha) t_stats, p_values, ci_lo, ci_hi = safe_inference_batch(qte, qte_ses, est.alpha) @@ -578,7 +622,7 @@ def _fit_distributional( results = ChangesInChangesResults( att=att, - se=att_se if (np.isfinite(att_se) and att_se > 0) else np.nan, + se=att_se, t_stat=t_stat, p_value=p_value, conf_int=conf_int, @@ -731,8 +775,13 @@ def __init__( self.is_fitted_ = False self.results_: Optional[ChangesInChangesResults] = None - def get_params(self) -> Dict[str, Any]: - """Return constructor hyperparameters (raw values, round-trips ``__init__``).""" + def get_params(self, deep: bool = True) -> Dict[str, Any]: + """Return constructor hyperparameters (raw values, round-trips ``__init__``). + + ``deep`` is accepted for sklearn compatibility (``sklearn.base.clone`` + calls ``get_params(deep=False)``) and is ignored - there are no nested + estimators. + """ return { "quantiles": self.quantiles, "n_bootstrap": self.n_bootstrap, @@ -833,8 +882,13 @@ def __init__( self.is_fitted_ = False self.results_: Optional[ChangesInChangesResults] = None - def get_params(self) -> Dict[str, Any]: - """Return constructor hyperparameters (raw values, round-trips ``__init__``).""" + def get_params(self, deep: bool = True) -> Dict[str, Any]: + """Return constructor hyperparameters (raw values, round-trips ``__init__``). + + ``deep`` is accepted for sklearn compatibility (``sklearn.base.clone`` + calls ``get_params(deep=False)``) and is ignored - there are no nested + estimators. + """ return { "quantiles": self.quantiles, "n_bootstrap": self.n_bootstrap, diff --git a/tests/test_changes_in_changes.py b/tests/test_changes_in_changes.py index 205896ff8..e4e6afba3 100644 --- a/tests/test_changes_in_changes.py +++ b/tests/test_changes_in_changes.py @@ -130,12 +130,19 @@ def test_fit_revalidates_after_direct_mutation(self, cls): with pytest.raises(ValueError, match="alpha"): fit_quiet(est, make_2x2()) + def test_get_params_deep_flag(self, cls): + # sklearn.base.clone calls get_params(deep=False); the flag is accepted + # and ignored (no nested estimators) - HAD precedent. + est = cls(n_bootstrap=9) + assert est.get_params() == est.get_params(deep=True) == est.get_params(deep=False) + def test_sklearn_clone_if_available(self, cls): - sklearn = pytest.importorskip("sklearn") + sklearn_base = pytest.importorskip("sklearn.base") est = cls(quantiles=[0.5], n_bootstrap=7, alpha=0.1, panel=True, seed=11) - clone = sklearn.base.clone(est) + clone = sklearn_base.clone(est) assert clone is not est assert clone.get_params() == est.get_params() + assert type(clone) is type(est) # ============================================================================= @@ -162,6 +169,21 @@ def test_formula_equals_kwargs(self, cls): r_f.quantile_effects["qte"].to_numpy(), r_kw.quantile_effects["qte"].to_numpy() ) + def test_formula_interaction_order_invariant(self, cls): + # 'treated:post' and 'post:treated' are algebraically the same formula; + # roles come from the main-effect order, so both must give identical + # results (CiC/QDiD are NOT symmetric in treatment/time). + df = make_2x2(seed=6) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r_a = cls(n_bootstrap=0).fit(df, formula="y ~ treated + post + treated:post") + r_b = cls(n_bootstrap=0).fit(df, formula="y ~ treated + post + post:treated") + r_kw = cls(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") + assert r_a.att == r_b.att == r_kw.att + np.testing.assert_array_equal( + r_a.quantile_effects["qte"].to_numpy(), r_b.quantile_effects["qte"].to_numpy() + ) + def test_formula_covariates_rejected(self, cls): with pytest.raises(ValueError, match="[Cc]ovariates"): cls(n_bootstrap=0).fit(make_2x2(), formula="y ~ treated * post + x1") @@ -193,6 +215,15 @@ def test_empty_cell_raises(self, cls): with pytest.raises(ValueError, match="Assumption 5.1"): fit_quiet(cls(n_bootstrap=0), df) + @pytest.mark.parametrize("bad", [np.inf, -np.inf]) + def test_nonfinite_outcome_raises(self, cls, bad): + # dropna() keeps inf; it must be rejected explicitly, never silently + # corrupt CDFs/quantiles/bootstrap (local review P1). + df = make_2x2() + df.loc[3, "y"] = bad + with pytest.raises(ValueError, match="non-finite"): + fit_quiet(cls(n_bootstrap=0), df) + def test_na_rows_dropped_with_warning(self, cls): df = make_2x2() df.loc[:4, "y"] = np.nan @@ -405,6 +436,31 @@ def test_clean_data_no_unexpected_warnings(self): ChangesInChanges(n_bootstrap=0).fit(df, outcome="y", treatment="treated", time="post") +class TestDegenerateBootstrap: + def test_zero_spread_replicates_nan_everywhere(self): + # Constant outcome within every cell: every bootstrap replicate is + # identical, so replicate SDs are exactly 0. The joint-NaN contract + # requires the STORED se to be NaN too (not 0.0) so that + # uniform_bands() cannot emit finite zero-width bands while pointwise + # inference is NaN (local review P0). + n = 40 + df = pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(np.repeat([1, 0], n // 2), 2), + } + ) + df["y"] = 1.0 * df["treated"] + 2.0 * df["post"] # constant within each cell + res = fit_quiet(QDiD(n_bootstrap=50, seed=3), df) + qe = res.quantile_effects + assert qe["se"].isna().all() + assert qe["t_stat"].isna().all() + assert np.isnan(res.se) + bands = res.uniform_bands() + assert bands["band_low"].isna().all() + assert bands["band_high"].isna().all() + + class TestBootstrapFailureGate: def test_tiny_cells_gate_to_nan(self): # 2 obs per cell: pooled RCS resampling frequently empties a cell. diff --git a/tests/test_changes_in_changes_parity.py b/tests/test_changes_in_changes_parity.py index 4fe690c5b..9ee633c7c 100644 --- a/tests/test_changes_in_changes_parity.py +++ b/tests/test_changes_in_changes_parity.py @@ -85,6 +85,37 @@ def test_metadata_versions_match(fixture): assert fixture["metadata"]["qte_version"] == QTE_VERSION +def test_default_grid_is_r_seq_bit_exact(fixture, probs): + """_DEFAULT_QUANTILES is pinned to R's exact seq(0.05, 0.95, 0.05) doubles. + + np.arange(0.05, 0.96, 0.05) differs from R at 5 of 19 indices by one ulp, + and type-1 selection is ulp-sensitive on n*p integer boundaries - so the + default is hardcoded and locked here against the R-stored fixture probs + (local review P1). + """ + from diff_diff.changes_in_changes import _DEFAULT_QUANTILES + + np.testing.assert_array_equal(_DEFAULT_QUANTILES, probs) + + +def test_default_grid_parity_end_to_end(fixture, probs): + """quantiles=None reproduces the golden qte-default results exactly.""" + scenario = fixture["scenarios"].get("smalln_2x2_n60") + if scenario is None: + pytest.skip("smalln_2x2_n60 missing from fixture") + df = _scenario_df(scenario) + est = ChangesInChanges(n_bootstrap=0) # default grid + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + res = est.fit(df, outcome="y", treatment="treat", time="period") + np.testing.assert_allclose( + res.quantile_effects["qte"].to_numpy(), + np.asarray(scenario["results"]["cic_rcs"]["qte"], dtype=float), + atol=POINT_ATOL, + rtol=0, + ) + + SCENARIOS = ["normal_2x2_n500", "lognormal_2x2_n300", "smalln_2x2_n60", "lalonde_psid"] RESULT_KEYS = ["cic_panel", "cic_rcs", "qdid_panel", "qdid_rcs"] @@ -154,7 +185,7 @@ class TestSEStatisticalParity: qte's bootstrap is unseeded through its public API, so the R draws cannot be replicated; both sides estimate the SD of the same resampling scheme).""" - @pytest.mark.parametrize("block_key", ["cic_panel", "cic_rcs", "qdid_panel"]) + @pytest.mark.parametrize("block_key", ["cic_panel", "cic_rcs", "qdid_panel", "qdid_rcs"]) def test_se_parity(self, fixture, probs, ci_params, block_key): block = fixture["se_block"].get(block_key) if block is None: From 9cb789ce29eba51b1371a75b77a54742d4b1d359 Mon Sep 17 00:00:00 2001 From: igerber Date: Sun, 12 Jul 2026 15:40:11 -0400 Subject: [PATCH 3/3] docs(todo): cite #682 in the CiC/QDiD deferral and follow-up rows Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_011hismLBFcUbUzvDRq8ruWb --- TODO.md | 14 +++++++------- diff_diff/__init__.py | 8 ++++---- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/TODO.md b/TODO.md index a3adba541..fab9a1923 100644 --- a/TODO.md +++ b/TODO.md @@ -47,8 +47,8 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| -| ChangesInChanges/QDiD tutorial notebook (2x2 distributional walkthrough: QTE grid, interior range, uniform bands, CiC-vs-QDiD comparison) - deferred from the implementation PR as a documented decision. | `docs/tutorials/` | CiC PR-B | Mid | Low | -| `practitioner_next_steps()` dedicated handler for `ChangesInChangesResults` (currently falls back to `_handle_generic`, which is safe; a dedicated handler is the established full-integration step, cf. HAD Phase 5). | `diff_diff/practitioner.py` | CiC PR-B | Quick | Low | +| ChangesInChanges/QDiD tutorial notebook (2x2 distributional walkthrough: QTE grid, interior range, uniform bands, CiC-vs-QDiD comparison) - deferred from the implementation PR as a documented decision. | `docs/tutorials/` | #682 | Mid | Low | +| `practitioner_next_steps()` dedicated handler for `ChangesInChangesResults` (currently falls back to `_handle_generic`, which is safe; a dedicated handler is the established full-integration step, cf. HAD Phase 5). | `diff_diff/practitioner.py` | #682 | Quick | Low | --- @@ -110,11 +110,11 @@ Doable in principle, but no current caller and/or explicitly out of paper scope. | Issue | Location | PR | Priority | |-------|----------|----|----------| -| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | - | Low | -| ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | - | Low | -| ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | - | Low | -| Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | - | Low | -| ChangesInChanges treatment-on-the-controls (Athey-Imbens Theorem 3.2: group-label exchange + negation; no qte equivalent to anchor parity). | `diff_diff/changes_in_changes.py` | - | Low | +| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | #682 | Low | +| ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | #682 | Low | +| ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | #682 | Low | +| Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | #682 | Low | +| ChangesInChanges treatment-on-the-controls (Athey-Imbens Theorem 3.2: group-label exchange + negation; no qte equivalent to anchor parity). | `diff_diff/changes_in_changes.py` | #682 | Low | | Rust-backend CR2 Bell-McCaffrey port (`return_dof` in the Rust vcov dispatch + CR2 algebra) — **premise re-scoped 2026-07-09**: the scores-based DOF + low-rank factored `A_g` changes made the NumPy CR2-BM path BLAS-bound (`O(n_g k²)` per cluster; 4.1s→38ms at n=100k/k=40), so a Rust port buys ~nothing and adds a parity surface. Revisit only if profiling shows CR2-BM hot again. | `rust/src/linalg.rs` | — | Low | | Clustered-CR1 inference df **default flip to `"cluster"` (G−1) at v4** — the opt-in `df_convention=` knob landed 2026-07 (DiD/TWFE/MPD + LinearRegression; REGISTRY §TwoWayFixedEffects deviation note); the remaining work is the major-version default change (moves every clustered p-value/CI) + migration note + flipping `TestDfConvention`/`test_moderate_t_pins_residual_df_convention` expectations. Also evaluate extending the knob to standalone estimators with CR1-t inference at that time. | `diff_diff/linalg.py::LinearRegression`, `diff_diff/estimators.py`, `diff_diff/twfe.py` | — | Medium | | CallawaySantAnna **unbalanced-panel R parity — LANDED** via `allow_unbalanced_panel=True` (matches R `did::att_gt(allow_unbalanced_panel=TRUE)` / `DRDID::reg_did_rc`: ATT bit-exact on cells AND dynamic aggregation via fixed unit-cohort-mass `pg` + a per-unit WIF; SE up to the documented CR1 `sqrt(G/(G-1))` factor). The earlier "weighting" framing was a mis-diagnosis — on unbalanced panels the dominant divergence from R is the *estimator* (within-cell differencing vs RC-on-pooled-obs), not only the weighting; both are resolved by the flag. The DEFAULT path keeps within-cell differencing as a documented design choice and now emits a `UserWarning` on unbalanced input (no-silent-failures). **Remaining deferred:** `survey_design=` × `allow_unbalanced_panel=` (per-obs vs per-unit weight resolution — currently fail-closed `NotImplementedError`); and covariate / ipw / dr × the flag R-parity verification (the RC path supports them; the committed golden covers `reg` no-cov). | `staggered.py`, `staggered_aggregation.py` | SE-audit D3 | Low | diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 124dd98bb..6d6d824f9 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -47,6 +47,10 @@ chaisemartin_dhaultfoeuille, twowayfeweights, ) +from diff_diff.chaisemartin_dhaultfoeuille_results import ( + ChaisemartinDHaultfoeuilleResults, + DCDHBootstrapResults, +) from diff_diff.changes_in_changes import ( ChangesInChanges, QDiD, @@ -55,10 +59,6 @@ ChangesInChangesResults, QDiDResults, ) -from diff_diff.chaisemartin_dhaultfoeuille_results import ( - ChaisemartinDHaultfoeuilleResults, - DCDHBootstrapResults, -) from diff_diff.continuous_did import ( ContinuousDiD, ContinuousDiDResults,