Implement CallawaySantAnna covariate adjustment for conditional parallel trends #22
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…lel trends Adds full covariate support to the CallawaySantAnna staggered DiD estimator: - Outcome regression (reg): Regresses outcome changes on covariates for control units, predicts counterfactual for treated - Inverse probability weighting (ipw): Estimates propensity scores via logistic regression, reweights control units to match treated covariate distribution - Doubly robust (dr): Combines both methods for consistent estimation if either the outcome model or propensity model is correctly specified Implementation details: - Added _logistic_regression() and _linear_regression() helper functions - Updated _outcome_regression(), _ipw_estimation(), and _doubly_robust() to accept and use covariate matrices - Modified _compute_att_gt() to extract covariates from base period data - Graceful fallback to unconditional estimation if covariate extraction fails Also includes: - 9 new tests for covariate adjustment across all estimation methods - Updated TODO.md to reflect completed implementation
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- Move scattered docstring notes to docs/reviews/pr22_covariate_adjustment_review.md - Keep actual code fixes: removed unused self._covariates, fixed IPW influence function - Keep new test for extreme propensity scores
igerber
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Changes based on PR #22 review: 1. Remove unused self._covariates instance variable - Was stored but never accessed; covariates passed through method chain 2. Fix empty influence function in unconditional IPW - Previously returned np.array([]) placeholder - Now properly computes influence function matching other methods 3. Add docstring example for covariate adjustment - Shows doubly robust usage with covariates 4. Add edge case tests: - test_extreme_propensity_scores: verifies clipping handles near-perfect separation - test_near_collinear_covariates: verifies lstsq handles ill-conditioned matrices - test_missing_values_in_covariates_warning: verifies fallback warning path
igerber
pushed a commit
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Jan 3, 2026
Changes based on PR #22 review: 1. Remove unused self._covariates instance variable - Was stored but never accessed; covariates passed through method chain 2. Fix empty influence function in unconditional IPW - Previously returned np.array([]) placeholder - Now properly computes influence function matching other methods 3. Add docstring example for covariate adjustment - Shows doubly robust usage with covariates 4. Add edge case tests: - test_extreme_propensity_scores: verifies clipping handles near-perfect separation - test_near_collinear_covariates: verifies lstsq handles ill-conditioned matrices - test_missing_values_in_covariates_warning: verifies fallback warning path
igerber
pushed a commit
that referenced
this pull request
Jan 3, 2026
Changes based on PR #22 review: 1. Remove unused self._covariates instance variable - Was stored but never accessed; covariates passed through method chain 2. Fix empty influence function in unconditional IPW - Previously returned np.array([]) placeholder - Now properly computes influence function matching other methods 3. Add docstring example for covariate adjustment - Shows doubly robust usage with covariates 4. Add edge case tests: - test_extreme_propensity_scores: verifies clipping handles near-perfect separation - test_near_collinear_covariates: verifies lstsq handles ill-conditioned matrices - test_missing_values_in_covariates_warning: verifies fallback warning path
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Adds full covariate support to the CallawaySantAnna staggered DiD estimator:
units, predicts counterfactual for treated
regression, reweights control units to match treated covariate distribution
the outcome model or propensity model is correctly specified
Implementation details:
accept and use covariate matrices
Also includes: