This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized settings. We present a formal decomposition of between-study heterogeneity, identifying sources of variability in treatment effects across studies. The proposed methodology allows for robust estimation of causal parameters under various assumptions, addressing differences in pre-treatment covariate distributions, mediating variables, and the outcome mechanism. Our approach is validated through a simulation study and applied to data from the Moving to Opportunity (MTO) study, demonstrating its practical relevance. This work contributes to the broader understanding of causal inference in multi-study environments, with potential applications in evidence synthesis and policy-making.

翻译：本文提出了一种新颖的分解框架，用于解释在不同研究（包括观察性研究和随机化研究）中观察到的因果效应异质性。我们提出了研究间异质性的形式化分解，识别了不同研究间处理效应变异性的来源。所提出的方法能够在各种假设下对因果参数进行稳健估计，并处理预处理协变量分布、中介变量及结果机制等方面的差异。我们通过模拟研究验证了该方法，并将其应用于“迁移至机会”（Moving to Opportunity, MTO）研究的数据，证明了其实用价值。这项工作有助于深化对多研究环境中因果推断的理解，在证据综合与政策制定方面具有潜在应用前景。