Conditional generative adversarial networks for individualized causal mediation analysis

Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine. Although identifying heterogeneous causal effects has received some attention, the causal effects are explored using the assumptive parametric models with limited model flexibility and analytic power. Recently, machine learning is becoming a major tool for accurately estimating individualized causal effects, thanks to its flexibility in model forms and efficiency in capturing complex nonlinear relationships. In this article, we propose a novel method, conditional generative adversarial network (CGAN) for individualized causal mediation analysis (CGAN-ICMA), to infer individualized causal effects based on the CGAN framework. Simulation studies show that CGAN-ICMA outperforms five other state-of-the-art methods, including linear regression, k-nearest neighbor, support vector machine regression, decision tree, and random forest regression. The proposed model is then applied to a study on the Alzheimer's disease neuroimaging initiative dataset. The application further demonstrates the utility of the proposed method in estimating the individualized causal effects of the apolipoprotein E-epsilon 4 allele on cognitive impairment directly or through mediators.