From the Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
Epidemiology. 2021 Mar 1;32(2):209-219. doi: 10.1097/EDE.0000000000001313.
Causal mediation analysis is a useful tool for epidemiologic research, but it has been criticized for relying on a "cross-world" independence assumption that counterfactual outcome and mediator values are independent even in causal worlds where the exposure assignments for the outcome and mediator differ. This assumption is empirically difficult to verify and problematic to justify based on background knowledge. In the present article, we aim to assist the applied researcher in understanding this assumption. Synthesizing what is known about the cross-world independence assumption, we discuss the relationship between assumptions for causal mediation analyses, causal models, and nonparametric identification of natural direct and indirect effects. In particular, we give a practical example of an applied setting where the cross-world independence assumption is violated even without any post-treatment confounding. Further, we review possible alternatives to the cross-world independence assumption, including the use of bounds that avoid the assumption altogether. Finally, we carry out a numeric study in which the cross-world independence assumption is violated to assess the ensuing bias in estimating natural direct and indirect effects. We conclude with recommendations for carrying out causal mediation analyses.
因果中介分析是流行病学研究的有用工具，但它因依赖于“跨世界”独立性假设而受到批评，即即使在因果世界中，结局和中介的暴露分配不同，反事实结局和中介值也是独立的。这个假设在经验上很难验证，并且基于背景知识难以证明其合理性。在本文中，我们旨在帮助应用研究人员理解这个假设。通过综合了解跨世界独立性假设，我们讨论了因果中介分析、因果模型和自然直接和间接效应的非参数识别的假设之间的关系。特别是，我们给出了一个应用环境的实际示例，即使没有任何事后混杂，跨世界独立性假设也会被违反。此外，我们还回顾了跨世界独立性假设的可能替代方法，包括使用完全避免该假设的界限。最后，我们进行了一项数值研究，其中违反了跨世界独立性假设，以评估估计自然直接和间接效应时随之而来的偏差。最后，我们提出了进行因果中介分析的建议。
