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：本文提出了一种新的分解框架，以解释在不同研究（包括观察性研究和随机化研究）中观测到的因果效应的异质性。我们给出了研究间异质性的正式分解，识别了不同研究中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，并处理处理前协变量分布、中介变量以及结果机制方面的差异。通过模拟研究对该方法进行了验证，并将其应用于“搬向机遇”（MTO）研究的数据，证明了其实践价值。本研究有助于在多研究环境中更深入地理解因果推断，并在证据合成与政策制定中具有潜在应用价值。