论文标题
在治疗/调解器内生性和结果损耗下直接和间接影响的界限
Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition
论文作者
论文摘要
因果中介分析旨在将治疗效果分解为通过中间结果或中介作用的间接机制，以及治疗对感兴趣结果的直接影响。但是，即使在控制观测值以及样本选择/结果损耗之后，直接和间接效应的评估也经常会使不可忽视的治疗和/或调解人的选择变得复杂。我们提出了一种在存在此类并发症的情况下使用基于一系列线性编程问题的方法来界定直接和间接效应的方法。考虑到倾向得分的反概率加权，我们计算了在没有并发症的情况下会产生识别的权重，并通过反映特定量倾向得分的熵参数扰动它们，以识别识别感兴趣的影响。我们将我们的方法应用于1979年青年纵向调查的数据，以根据性别工资差距分解的解释和无法解释的组成部分得出界限，该分解可能容易受到不可异常的调解人的选择和结果流失。
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment and/or mediator, even after controlling for observables, as well as sample selection/outcome attrition. We propose a method for bounding direct and indirect effects in the presence of such complications using a method that is based on a sequence of linear programming problems. Considering inverse probability weighting by propensity scores, we compute the weights that would yield identification in the absence of complications and perturb them by an entropy parameter reflecting a specific amount of propensity score misspecification to set-identify the effects of interest. We apply our method to data from the National Longitudinal Survey of Youth 1979 to derive bounds on the explained and unexplained components of a gender wage gap decomposition that is likely prone to non-ignorable mediator selection and outcome attrition.
