论文标题
一种新的分解，以解释因果关系的观测和随机研究中的异质性
A novel decomposition to explain heterogeneity in observational and randomized studies of causality
论文作者
论文摘要
本文介绍了一个新型的分解框架，以解释在不同研究中观察到的因果效应的异质性，考虑到观察和随机环境。我们提出了研究间异质性的形式分解，确定了整个研究的治疗效果的可变性来源。所提出的方法允许在各种假设下对因果参数进行稳健的估计，从而解决了治疗前协变量分布的差异，中介变量和结果机制。我们的方法通过仿真研究验证，并应用于移动到机会（MTO）研究的数据，证明了其实际相关性。这项工作有助于更广泛地理解多研究环境中因果推断，并在证据综合和决策中的潜在应用。
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.
