Adaptive discovery of effect modification in matched observational studies

2026-05-10

AI Summary

This study addresses the challenge of identifying heterogeneous treatment effects while controlling the false discovery rate (FDR) in matched observational studies, where limited sample sizes and unmeasured confounding pose significant obstacles. The authors propose a novel method that adaptively discovers interpretable subgroups defined by covariate thresholds under many-to-one matching designs. Their approach achieves exact FDR control at the subgroup level for the first time and integrates sensitivity analysis models to account for unobserved confounding, leveraging multiple controls to enhance statistical power. Theoretical analysis, simulations, and empirical evaluation demonstrate that the method outperforms existing baselines in both accuracy and power when estimating heterogeneous economic returns to college education.

Abstract

Understanding effect modification -- how treatment effects vary across subpopulations -- is practically important in observational studies, as it helps identify which subgroups are likely to benefit from a given treatment. In this paper, we study the discovery of effect modification in matched observational studies, where each treated unit may be matched to multiple controls. We develop a finite-sample valid procedure for identifying and selecting covariate-interpretable subgroups, with exact control of the subgroup-level false discovery rate (FDR). Our method explicitly accounts for unmeasured confounding via sensitivity models, and leverages multiple matched controls to improve statistical power. We demonstrate the favorable performance of our method relative to baseline methods through extensive simulation studies and a real-world application to the economic returns to college education.

Problem

• effect modification
• observational studies
• matched controls
• false discovery rate
• subgroup identification

Innovation

• effect modification
• matched observational studies
• false discovery rate
• unmeasured confounding
• multiple controls