Causal Mediation Analysis for Effect Heterogeneity

Understanding the impact of an exposure on an outcome through a mediator is crucial in many fields of study. However, the effect often varies across subgroups. Addressing why and how an exposure gives rise to an outcome differently for subsets provides a deeper understanding about what works for whom through which intermediate(s). This paper explores causal mechanisms by focusing on explaining effect heterogeneity via causal mediation analysis. The counterfactual framework is extended to heterogeneous effect decomposition. We provide nonparametric definitions, identification assumptions, and analytical formulas for various direct and indirect effect heterogeneity measures. The decomposition of effect heterogeneity provides insightful causal implications about the question for whom and in what context that the effect operates. The proposed methodology is demonstrated through an application of effect heterogeneity decomposition of neighborhood poverty on mental health, modified by gender, using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health).