A finite-sample valid method discovers and selects covariate-interpretable subgroups with effect modification in matched observational studies, exactly controlling subgroup-level FDR and incorporating sensitivity analysis for unmeasured confounding.
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Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
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Adaptive discovery of effect modification in matched observational studies
A finite-sample valid method discovers and selects covariate-interpretable subgroups with effect modification in matched observational studies, exactly controlling subgroup-level FDR and incorporating sensitivity analysis for unmeasured confounding.
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Active Hypothesis Testing under Computational Budgets with Applications to GWAS and LLM
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.