Improve se accuracy wif#63
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Implement weight influence function (wif) adjustment for the "simple" aggregation in CallawaySantAnna, matching R's `did` package approach. Changes: - Add _compute_aggregated_se_with_wif() method that accounts for uncertainty in estimating group-size weights - Update _aggregate_simple() to use the new wif-adjusted SE calculation - Add tests/test_se_accuracy.py for SE accuracy testing and regression Results: - SE difference reduced from ~2.5% to ~1.2% across all scales - Point estimates unchanged (ATT diff < 1e-10) - No performance regression - All existing tests pass Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The weight influence function (wif) formula had two issues causing ~1.1% SE difference vs R: 1. pg computation: Used n_g / total_treated instead of n_g / n_all 2. wif iteration: Iterated over groups with averaged ATT instead of keepers (post-treatment pairs) with individual ATT(g,t) values Now implements R's exact formula: - if1[i,k] = (indicator(G_i == group_k) - pg[k]) / sum(pg[keepers]) - if2[i,k] = indicator_sum[i] * pg[k] / sum(pg[keepers])^2 - wif[i,k] = if1[i,k] - if2[i,k] - wif_contrib[i] = sum_k(wif[i,k] * att[k]) Result: SE now matches R within <0.01% (essentially exact match). Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The test_se_vs_r_benchmark test requires specific benchmark data with known R values. This data is generated locally and not committed to the repo. The test now skips gracefully in CI with a helpful message. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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