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.

翻译：理解效应修饰——即治疗效果在不同亚群间的变化规律——在观察性研究中具有重要实践意义，因为它有助于识别哪些亚组可能从特定治疗中获益。本文研究匹配观察性研究中的效应修饰发现，其中每个处理单元可能与多个对照单元匹配。我们开发了一种有限样本有效程序，用于识别和选择具有协变量可解释性的亚组，并精确控制亚组水平的错误发现率（FDR）。该方法通过敏感性模型明确考虑未测量的混杂因素，并利用多重匹配对照来提高统计功效。通过广泛的模拟研究以及一项关于大学教育经济回报的真实世界应用，我们验证了该方法相对于基线方法的优越性能。