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Add EfficientDiD estimator (Chen, Sant'Anna & Xie 2025, Phase 1)
Implement the semiparametrically efficient ATT estimator for DiD with staggered treatment adoption (no-covariates path). The estimator achieves the efficiency bound by optimally weighting across pre-treatment periods and comparison groups via the inverse of the within-group covariance matrix Omega*. Under PT-All the model is overidentified and EDiD exploits this for tighter inference; under PT-Post it reduces to standard CS. New files: - efficient_did.py: main EfficientDiD class with sklearn-like API - efficient_did_weights.py: Omega* matrix, efficient weights, EIF - efficient_did_bootstrap.py: multiplier bootstrap mixin - efficient_did_results.py: EfficientDiDResults dataclass - tests/test_efficient_did.py: 42 tests across 4 tiers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎diff_diff/__init__.py‎

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ContinuousDiDResults,
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DoseResponseCurve,
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)
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from diff_diff.efficient_did import (
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EfficientDiD,
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EfficientDiDResults,
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EDiDBootstrapResults,
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)
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from diff_diff.trop import (
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TROP,
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TROPResults,
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DDD = TripleDifference
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Stacked = StackedDiD
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Bacon = BaconDecomposition
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EDiD = EfficientDiD
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__version__ = "2.6.1"
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__all__ = [
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"trop",
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"StackedDiDResults",
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"stacked_did",
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# EfficientDiD
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"EfficientDiD",
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"EfficientDiDResults",
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"EDiDBootstrapResults",
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"EDiD",
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# Visualization
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"plot_event_study",
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"plot_group_effects",

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