From bf426c7cb337e070420368438130852d623608e9 Mon Sep 17 00:00:00 2001 From: igerber Date: Tue, 2 Jun 2026 06:40:30 -0400 Subject: [PATCH] Bump version to 3.5.1 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Promote the [Unreleased] CHANGELOG block to [3.5.1] (2026-06-02) and sync the version string across pyproject.toml, rust/Cargo.toml, diff_diff/__init__.py, diff_diff/guides/llms-full.txt, and CITATION.cff (date-released 2026-06-02). Patch release since v3.5.0 — enhancements, validations, and fixes to existing surfaces (no new estimator): SyntheticControl cross-validation + inverse-variance V-selection (#523); Firpo & Possebom (2018) SCM-inference paper review (#524); HeterogeneousAdoptionDiD fit() UX warnings (#525); covariate-name collision now raises ValueError (#518); EfficientDiD methodology-review promotion to Complete with sieve outcome-regression upgrade (#521); AI PR-reviewer model gpt-5.4 -> gpt-5.5 (#522). Co-Authored-By: Claude Opus 4.8 (1M context) --- CHANGELOG.md | 3 +++ CITATION.cff | 4 ++-- diff_diff/__init__.py | 2 +- diff_diff/guides/llms-full.txt | 2 +- pyproject.toml | 2 +- rust/Cargo.toml | 2 +- 6 files changed, 9 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 27c7aeb9..17058617 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [3.5.1] - 2026-06-02 + ### Added - **`SyntheticControl` cross-validation + inverse-variance `V`-selection (ADH 2015 §; Abadie 2021 §3.2(a), Eq. 9).** Two new `v_method` values complete the ADH-2015/Abadie-2021 `V`-selection menu (joining `"nested"` / `"custom"`), each threaded through the in-space / leave-one-out / in-time placebo refits so a diagnostic uses the **same** estimator as the headline fit. **`v_method="cv"`** selects the diagonal predictor-importance `V` by out-of-sample cross-validation: the pre-period is split positionally at `v_cv_t0` (new constructor param; default `len(pre)//2`, Abadie 2021's `t0 = T0/2`) into a training and a validation window, `V` is chosen to minimize the validation-window outcome MSPE of the training-fit weights (`mspe_v` now reports this validation MSPE under cv), and the final reported weights are re-estimated on the validation-window predictors (ADH 2015 step 4). Each predictor spec is **re-aggregated** over each window (its mean/sum/identity recomputed over only the periods that fall in that window — a separate `dataprep` per window, exactly as ADH 2015's CV does, since R `Synth` has no built-in CV function), so the V-search is genuinely out-of-sample for every predictor type and the same `V*` drives both fits with no zeroed coordinate (`v_weights` reproduce `donor_weights` on the validation-window predictors, and `predictor_balance` is reported on that validation-window basis). **Fully-spanning precondition (fail-closed):** re-aggregating a predictor on each window requires it to be observed in **both** windows, so `cv` **requires every predictor to span both the training and validation windows** and raises `ValueError` otherwise — satisfied by ADH 2015's shared covariate / multi-period `special_predictors` (which span the windows) but NOT by the default per-period outcome lags (each is single-period and lives in one window only), so `cv` with the bare default predictors is rejected with guidance to pass spanning predictors. In-time-placebo truncation that breaks the fully-spanning precondition (a kept spec stops spanning both windows at the truncated split) marks that date `infeasible`. A second fail-closed gate covers windows that span but carry **no cross-donor variation** (every re-aggregated predictor constant across the donors, so `X0·W` is constant in `W` → a flat, unidentified weight solve that would otherwise return arbitrary "converged" weights — even when the treated unit differs, since donor distinguishability, not treated-vs-donor variation, identifies `W`): the headline fit raises `ValueError`, in-space placebo refits whose donor pool is indistinguishable in a window are dropped from the reference set, and such in-time-truncated dates are marked `infeasible`. Abadie 2021 footnote 7's CV non-uniqueness is handled by a **deterministic tie-break** (prefer the `V` closest to uniform among ties), making the selected `V*` among equally-good optima independent of the multistart evaluation order. The cv fit is reproducible for a fixed `seed` (like `nested`) but is not seed-independent — the multistart fills any slots beyond the distinct heuristic starts with seed-dependent random Dirichlet draws, so the tie-break removes start-order dependence among ties, not seed dependence. The tie-break is convergence-aware (a non-converged optimizer candidate cannot displace a converged incumbent on an objective tie). If the training-window solve that defines `mspe_v` truncates (e.g. `inner_max_iter` too small), the fit fails closed — `mspe_v=NaN` and the fit is marked non-converged — rather than reporting an invalid Eq. 9 criterion. **`v_method="inverse_variance"`** uses the closed form `v_h = 1/Var(X_h)` (variance over donors+treated on the unstandardized predictors), applied to the **raw** predictors so the effective objective is the unit-variance-rescaled `Σ_h diff_h²/Var_h` (Abadie 2021 §3.2(a)); the `standardize` pre-scaling is intentionally bypassed on this branch (inverse-variance weighting *is* the unit-variance rescaling — applying it on already-standardized rows would double-rescale to `Σ_h diff_h²/Var_h²`), so it is equivalent to uniform `V` on standardized predictors. No search (`mspe_v=None`); a zero-variance row gets 0 weight and an all-zero-variance panel falls back to uniform `V` with a warning. `custom_v` is rejected (fail-closed) for both methods and `v_cv_t0` is rejected unless `v_method="cv"`. On the degenerate **single-donor** path (`J=1` forces `w=[1]`) `V` is unidentified — every `V` yields the same synthetic — so `v_weights` is **uniform** and `mspe_v=None` for ALL `v_method`s (cv / inverse_variance included; their selected / closed-form `V` would be inert), with a `UserWarning`; the donor weights / gap / ATT are unaffected. An explicitly pinned `v_cv_t0` that no longer fits the truncated pre-fake window is nulled to the `//2` default for the placebo refit (a pinned value that still fits the truncated window is kept). **Validation:** R `Synth` has no built-in CV function (ADH 2015's CV is a manual `dataprep`+`synth` re-run), so cv is anchored by deterministic equivalence to the R-anchored `custom_v` path (the step-3 validation MSPE of the training-window fit and the step-4 validation-window weights each match a `custom_v=V*` fit on the correspondingly re-aggregated predictors) plus cv self-consistency (`in_time_placebo` under cv == a fresh cv fit on the backdated panel to 1e-7); inverse-variance is anchored bit-for-bit to a `custom_v=1/Var(X)` fit. Documented in `docs/methodology/REGISTRY.md` §SyntheticControl (new `**Note:**` labels for the per-window re-aggregation convention, the flat-MSPE tie-break, and inverse-variance), `docs/api/synthetic_control.rst`, the LLM guides, and `README.md`. The remaining ADH-2015 items (`W^reg` extrapolation diagnostic, sparse-SC subset search) stay tracked in `TODO.md`. - **Firpo & Possebom (2018) SCM inference paper review on file (PR-A).** Added `docs/methodology/papers/firpo-possebom-2018-review.md`, a faithful, paper-sourced fidelity review of Firpo & Possebom (2018, *Journal of Causal Inference* 6(2), DOI 10.1515/jci-2016-0026) — the Step-1 artifact for the forthcoming SCM **confidence-set / CI-by-test-inversion** track (PR-B) layered on the existing `SyntheticControl` estimator (classic SCM has no analytical SE; `se`/`p_value`/`conf_int` are NaN). Transcribes (paper-sourced only, no code-deviation verdicts) the benchmark RMSPE-ratio permutation test (Eqs. 4–6), the sensitivity-analysis parametric p-value weights with worst/best-case `φ̲`/`φ̄` (Eqs. 7–9), the sharp-null `RMSPE^f` test (Eqs. 10–13), the **confidence sets by test inversion** (Eq. 14) with the operational constant-effect CI (Eqs. 15–16) and linear-effect CS (Eqs. 17–18), the general test-statistic framework + Monte Carlo size/power of five statistics (Eq. 19, Section 5), and the multiple-outcome FWER (Eqs. 23–24) and multiple-treated-unit pooled (Eqs. 25–26) extensions; the requirements checklist flags the PR-B target (sharp-null test + constant/linear CI + benchmark + one-sided) versus the deferred sensitivity-analysis and multi-outcome/treated extensions. Docs-only; no code change. Registered in `docs/references.rst` (Synthetic Control Method section) and `docs/doc-deps.yaml`; REGISTRY `## SyntheticControl` gains a `firpo-possebom-2018-review.md` reviews-on-file pointer. @@ -1553,6 +1555,7 @@ for the full feature history leading to this release. [2.1.2]: https://github.com/igerber/diff-diff/compare/v2.1.1...v2.1.2 [2.1.1]: https://github.com/igerber/diff-diff/compare/v2.1.0...v2.1.1 [2.1.0]: https://github.com/igerber/diff-diff/compare/v2.0.3...v2.1.0 +[3.5.1]: https://github.com/igerber/diff-diff/compare/v3.5.0...v3.5.1 [3.5.0]: https://github.com/igerber/diff-diff/compare/v3.4.2...v3.5.0 [3.4.2]: https://github.com/igerber/diff-diff/compare/v3.4.1...v3.4.2 [3.4.1]: https://github.com/igerber/diff-diff/compare/v3.4.0...v3.4.1 diff --git a/CITATION.cff b/CITATION.cff index 1e3c3278..f9262ffe 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -7,8 +7,8 @@ authors: family-names: Gerber orcid: "https://orcid.org/0009-0009-3275-5591" license: MIT -version: "3.5.0" -date-released: "2026-06-01" +version: "3.5.1" +date-released: "2026-06-02" doi: "10.5281/zenodo.19646175" url: "https://github.com/igerber/diff-diff" repository-code: "https://github.com/igerber/diff-diff" diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index ca5cdda9..8e9da045 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -298,7 +298,7 @@ DCDH = ChaisemartinDHaultfoeuille HAD = HeterogeneousAdoptionDiD -__version__ = "3.5.0" +__version__ = "3.5.1" __all__ = [ # Estimators "DifferenceInDifferences", diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index b4a89596..1bf09c2b 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -2,7 +2,7 @@ > A Python library for Difference-in-Differences causal inference analysis. Provides sklearn-like estimators with statsmodels-style output for econometric analysis. -- Version: 3.5.0 +- Version: 3.5.1 - Repository: https://github.com/igerber/diff-diff - License: MIT - Dependencies: numpy, pandas, scipy (no statsmodels dependency) diff --git a/pyproject.toml b/pyproject.toml index 9c70d82d..81be1f41 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "maturin" [project] name = "diff-diff" -version = "3.5.0" +version = "3.5.1" description = "Difference-in-Differences causal inference with sklearn-like API. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies, parallel trends." readme = "README.md" license = "MIT" diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 1ad3c9a5..24bad2ff 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "diff_diff_rust" -version = "3.5.0" +version = "3.5.1" edition = "2021" rust-version = "1.85" description = "Rust backend for diff-diff DiD library"