This paper extends the evaluation of direct and indirect treatment effects, i.e., mediation analysis, to the case that outcomes are only partially observed due to sample selection or outcome attrition. We assume sequential conditional independence of the treatment and the mediator, i.e., the variable through which the indirect effect operates. We also impose missing at random or instrumental variable assumptions on the outcome attrition process. Under these conditions, we derive identification results for the effects of interest that are based on inverse probability weighting by specific treatment, mediator, and/or selection propensity scores. We also provide a simulation study and an empirical application to the U.S. Project STAR data in which we assess the direct impact and indirect effect (via absenteeism) of smaller kindergarten classes on math test scores. The estimators considered are available in the ‘causalweight’ package for the statistical software ‘R’.

中文翻译：

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样本选择和结果耗损下的直接和间接影响

本文将对直接和间接治疗效果（即调解分析）的评估扩展到由于样本选择或结果耗损而仅部分观察到结果的情况。我们假设治疗和调解人的顺序条件独立性，即间接作用通过其起作用的变量。我们还对结果耗损过程施加了随机或工具变量假设上的缺失。在这些条件下，我们根据特定处理，中介和/或选择倾向得分的逆概率加权，得出感兴趣效果的识别结果。我们还为美国提供了模拟研究和经验应用 项目STAR数据，我们在其中评估了较小的幼儿园班对数学测验成绩的直接影响和间接影响（通过缺勤）。统计软件“ R”的“因果重量”软件包中提供了所考虑的估计量。