Add Synthetic Difference-in-Differences (SDID) estimator#8
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Implements the Synthetic DiD method from Arkhangelsky et al. (2021) that combines DiD with synthetic control by re-weighting control units to match treated units' pre-treatment outcomes. Key features: - SyntheticDiD estimator class with sklearn-like API - Unit weight optimization via projected gradient descent - Time weight computation for emphasizing informative periods - Bootstrap and placebo-based inference - SyntheticDiDResults with weight inspection methods - Comprehensive test suite (30+ new tests) - Documentation with use cases (few treated units, questionable parallel trends, heterogeneous controls) Bumps version to 0.3.0.
Add comprehensive bibliography covering all techniques implemented in the library: - Difference-in-Differences (Ashenfelter & Card, Card & Krueger, Angrist & Pischke) - Two-Way Fixed Effects (Wooldridge, Imai & Kim) - Robust Standard Errors (White, MacKinnon & White, Cameron et al.) - Synthetic Control Method (Abadie et al.) - Synthetic Difference-in-Differences (Arkhangelsky et al.) - Parallel Trends Testing (Roth, Rambachan & Roth) - Multi-Period/Staggered DiD (Callaway & Sant'Anna, Sun & Abraham, de Chaisemartin) - General Causal Inference (Imbens & Rubin, Cunningham)
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…StageDiD Addresses axis-C findings #8, #9, and #10 from the silent-failures audit: three sites where a sparse factorization failure silently fell back to dense lstsq without any user-facing signal. - diff_diff/imputation.py:1516 (variance path: scipy.sparse.linalg.spsolve on (A_0' W A_0) z = A_1' w). Bare `except Exception` was swallowing the root cause before dense lstsq. Now emits a UserWarning identifying the exception type and explaining the fallback implication. - diff_diff/two_stage.py:1647 (GMM sandwich: sparse_factorized on X'_{10} W X_{10} for Stage 1 normal equations). `except RuntimeError` was silent; now emits a UserWarning. - diff_diff/two_stage_bootstrap.py:134 (bootstrap path: same pattern as above). `except RuntimeError` was silent; now emits a UserWarning. All three are single-call sites (per fit, or per aggregation level, or per bootstrap replicate at most a handful of times) so no aggregation wrapper pattern is needed — one warning per fallback event is appropriate. REGISTRY.md updated under ImputationDiD and TwoStageDiD. New tests (3): monkey-patch the sparse entry point to raise a RuntimeError, run .fit(), assert the UserWarning fires with the expected message prefix. Works against both the variance and bootstrap surfaces. Axis-C baseline: 3 major silent-fallback sites (imputation, two_stage, two_stage_bootstrap) -> 0 remaining in these files. PowerAnalysis simulation counter (finding #11) and ContinuousDiD B-spline (#12) still open as separate follow-ups. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Implements the Synthetic DiD method from Arkhangelsky et al. (2021) that
combines DiD with synthetic control by re-weighting control units to match
treated units' pre-treatment outcomes.
Key features:
trends, heterogeneous controls)
Bumps version to 0.3.0.