Confounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology
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Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (PM2.5) increases mortality risk. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify mutually exclusive groups of the population that are more or less vulnerable to air pollution. In the causal inference literature, the Conditional Average Treatment Effect (CATE) is a commonly used metric designed to characterize the heterogeneity of a treatment effect based on some population characteristics. In this work, we introduce a novel modeling approach, called Confounder-Dependent Bayesian Mixture Model (CDBMM), to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the Dependent Dirichlet Process to model the distribution of the potential outcomes conditionally to the covariates, thus enabling us to: (i) estimate individual treatment effects, (ii) identify heterogeneous and mutually exclusive population groups defined by similar CATEs, and (iii) estimate causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found seven mutually exclusive groups where the causal effects of PM2.5 on mortality are heterogeneous.
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