论文标题
通过多种治疗和潜在混杂因素的因果调解分析
Causal Mediation Analysis with Multiple Treatments and Latent Confounders
论文作者
论文摘要
因果中介分析用于评估治疗方法通过中间变量或中介者对感兴趣结果的直接和间接因果作用。它很难识别直接和间接的因果效应，因为在许多真实应用中不能随机分配介体。在本文中，我们考虑了一个因果模型，其中包括调解人与结果之间的潜在混杂因素。我们提出了足够的条件来识别直接和间接效应，并提出了一种估计它们的方法。通过模拟研究评估了所提出的方法的性能。最后，我们将方法应用于电信公司的客户忠诚度调查的数据集。
Causal mediation analysis is used to evaluate direct and indirect causal effects of a treatment on an outcome of interest through an intermediate variable or a mediator.It is difficult to identify the direct and indirect causal effects because the mediator cannot be randomly assigned in many real applications. In this article, we consider a causal model including latent confounders between the mediator and the outcome. We present sufficient conditions for identifying the direct and indirect effects and propose an approach for estimating them. The performance of the proposed approach is evaluated by simulation studies. Finally, we apply the approach to a data set of the customer loyalty survey by a telecom company.
