A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
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Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
citing papers explorer
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In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
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Causal Stability Selection
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.