A Causal Inference Approach for Mediated Moderation with Multiple Mediators

Understanding how effect heterogeneity across subgroups is mediated by multiple mediators is important yet under-studied. Despite the growing popularity of causal mediation analysis, existing methods rarely address the mediation of moderated treatment effects, particularly when multiple mediators are of interest. This study develops a causal inference approach for mediated moderation analysis with multiple mediators, decomposing a moderated treatment effect into mediated moderation effects and remaining moderation. We present causal estimands and extend a multiply robust estimator that can incorporate machine learning techniques to relax modeling assumptions. Simulations were conducted to evaluate the method’s performance. An empirical example about adolescent mental health illustrates the application. We hope this study provides a novel causal inference-based approach to understanding multiple mediating mechanisms underlying subgroup heterogeneity in treatment effects.