论文标题
洞悉因果中介分析的“跨世界”独立性假设
Insights into the "cross-world" independence assumption of causal mediation analysis
论文作者
论文摘要
因果中介分析是流行病学研究的有用工具，但它因依靠“跨世界”的独立性假设而受到批评，该假设在经验上很难验证和有问题，无法根据背景知识来证明合理性。在本文中，我们旨在帮助应用研究人员理解这一假设。综合有关跨世界独立性假设的知识，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鉴定之间的关系。特别是，我们给出了一个实用的例子，说明了一个应用的环境，即使没有任何治疗后，跨世界独立性假设也会受到侵犯。此外，我们回顾了跨世界独立性假设的可能替代方法，包括使用完全避免假设的界限的计算。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，以评估随之而来的自然直接和间接效应时的偏见。我们最终提出了进行因果关系分析的建议。
Causal mediation analysis is a useful tool for epidemiological research, but it has been criticized for relying on a "cross-world" independence assumption that 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 non-parametric 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 computation of bounds that avoid the assumption altogether. Finally, we carry out a numerical 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.
