The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
PermuCATE applies conditional permutation importance to CATE estimation, claiming lower variance and higher statistical power than LOCO on simulated and health datasets.