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Several epidemiological studies have provided evidence that long-termexposure to fine particulate matter (PM2.5) increases mortality risk.Furthermore, some population characteristics (e.g., age, race, andsocioeconomic status) might play a crucial role in understanding vulnerabilityto air pollution. To inform policy, it is necessary to identify groups of thepopulation that are more or less vulnerable to air pollution. In causalinference literature, the Group Average Treatment Effect (GATE) is adistinctive facet of the conditional average treatment effect. This widelyemployed metric serves to characterize the heterogeneity of a treatment effectbased on some population characteristics. In this work, we introduce a novelConfounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causaleffect heterogeneity. More specifically, our method leverages the flexibilityof the dependent Dirichlet process to model the distribution of the potentialoutcomes conditionally to the covariates and the treatment levels, thusenabling us to: (i) identify heterogeneous and mutually exclusive populationgroups defined by similar GATEs in a data-driven way, and (ii) estimate andcharacterize the causal effects within each of the identified groups. Throughsimulations, we demonstrate the effectiveness of our method in uncovering keyinsights about treatment effects heterogeneity. We apply our method to claimsdata from Medicare enrollees in Texas. We found six mutually exclusive groupswhere the causal effects of PM2.5 on mortality are heterogeneous.

Confounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology