论文标题
两个调解人的总效应的分解：自然反事实互动效果框架
Decomposition of the Total Effect for Two Mediators: A Natural Counterfactual Interaction Effect Framework
论文作者
论文摘要
在许多学科中，都使用了调解分析来解释通过介导子的包含在曝光变量和结果变量之间观察到的关系的机制或过程。在过去的十年中，暴露变量变量的总因果变量分解对表征调解途径和相互作用的影响越来越多。在这项工作中，我们为两个中介者是因果关系或非序列而开发的分解。该领域的当前发展主要集中在没有相互作用组件或相互作用的没有相互作用的分解上，但假设介体之间没有因果关系顺序。我们提出了一个称为自然反事实互动效应的新概念，该效果捕获了两种情况扩展文献中双向介导的相互作用的双向和三向相互作用。我们开发了一种将总效应分解为仅由于调解，仅相互作用，中介和相互作用，中介和反事实框架内的相互作用而引起的统一方法。最后，我们使用实际数据分析说明了提出的分解方法，其中两个介体是因果关系的。
Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total causal effect of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where the two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural counterfactual interaction effect that captures the two-way and three-way interactions for both scenarios that extend the two-way mediated interactions in literature. We develop a unified approach for decomposing the total effect into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we illustrate the proposed decomposition method using a real data analysis where the two mediators are causally sequential.
