Identifiability and estimation of causal mediation effects with missing data

Mediation analysis is a standard approach to understanding how and why an intervention works in social and medical sciences. However, the presence of missing data, especially missing not at random data, poses a great challenge for the applicability of this approach in practice. Current methods for handling such missingness are still lacking in causal mediation analysis. In this article, we first show the identifiability of causal mediation effects with different types of missing outcomes under different missingness mechanisms. We then provide corresponding approaches for estimation and inference. Especially for missing not at random data, we develop an estimating equation–based approach to estimate causal mediation effects, which can easily handle different types of mediators and outcomes, and we also establish the asymptotic results of the estimators. Simulation results show good performance for the proposed estimators in finite samples. Finally, we use a real data set from the Clinical Antipsychotic Trials of Intervention Effectiveness Research for Alzheimer disease to illustrate our approach.