Optimize CallawaySantAnna WIF computation with vectorized NumPy operations#65
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…tions Vectorize the weight influence function (WIF) computation in _compute_aggregated_se_with_wif() to achieve 4-10x speedup over R at all dataset scales while maintaining exact SE accuracy (0.0% difference). Changes: - Replace O(n_units × n_keepers) nested loops with NumPy broadcasting - Use precomputed unit-group mapping instead of O(n) DataFrame lookups - Vectorize influence function aggregation with np.add.at() - Add indicator_matrix, if1/if2 matrix operations, and matrix multiply for wif_contrib = wif_matrix @ effects Performance improvements (vs R's did package): - small: 2.7x → 10.5x faster - 1k: 1.0x → 8.8x faster - 5k: 0.5x → 6.8x faster - 10k: 0.4x → 5.3x faster - 20k: 0.3x → 4.2x faster SE accuracy remains at 0.0% difference from R at all scales. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Reviews the vectorized WIF computation changes: - Methodology correctness: PASS (mathematical equivalence verified) - Code quality: PASS (clean vectorization, proper edge handling) - Performance: PASS (4-10x faster than R, 0.0% SE difference) - Tech debt: MINIMAL (clean extension of existing patterns) Final verdict: APPROVE
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Vectorize the weight influence function (WIF) computation in _compute_aggregated_se_with_wif() to achieve 4-10x speedup over R at all dataset scales while maintaining exact SE accuracy (0.0% difference).
Changes:
Performance improvements (vs R's did package):
SE accuracy remains at 0.0% difference from R at all scales.