Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the mediator and outcome are missing not at random, the direct and indirect effects are not identifiable without further assumptions. In this work, we study the identifiability of the direct and indirect effects under some interpretable mechanisms that allow for missing not at random in the mediator and outcome. We evaluate the performance of statistical inference under those mechanisms through simulation studies and illustrate the proposed methods via the National Job Corps Study.

翻译：中介效应分析广泛应用于探究效应产生的直接和间接因果路径。然而，许多中介效应分析研究面临中介变量和结局变量缺失的挑战。通常情况下，当中介变量和结局变量非随机缺失时，若无额外假设，直接效应和间接效应无法被识别。本研究探讨了在允许中介变量和结局变量非随机缺失的若干可解释机制下，直接效应和间接效应的可识别性。我们通过模拟研究评估了这些机制下统计推断的性能，并借助美国国家职业训练团研究项目对提出的方法进行了实例说明。