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arXiv · 2001.04040

Causal Mediation Analysis with Multiple Treatments and Latent Confounders

Wei Li·Chunchen Liu·Zhi Geng·John Murray
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Abstract

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.

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Wei Li, Chunchen Liu, Zhi Geng, John Murray. 2020-01-13. Causal Mediation Analysis with Multiple Treatments and Latent Confounders. https://arxiv.org/abs/2001.04040

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