Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects

Environmental accountability studies evaluate whether regulatory interventions achieve their intended benefits. These studies fall into two broad categories: indirect accountability, which estimates the effects of exposure to higher levels of air pollution and uses them to indirectly predict policy impacts, and direct accountability, which evaluates specific interventions directly. The purpose of this review is to evaluate recent advances in causal inference and flexible Bayesian statistical modeling to support environmental accountability studies.

Recent studies, in both direct and indirect environmental accountability, deeply rely on causal inference to produce robust inferences and provide relevant and actionable insights to policy-makers and practitioners. In this context, heterogeneous treatment effects are central to both approaches as it is critically important to understand which subpopulations are most vulnerable (or resilient), requiring methods that can characterize effect variation. Bayesian nonparametric (BNP) methods have been proposed for this task due to their flexibility and ability to cluster units into coherent subgroups. We present a selection of BNP methods designed or adapted for each type of accountability, focusing on complementary approaches based on Bayesian Additive Regression Trees (BART) and Dependent Dirichlet Processes (DDP). Through Monte Carlo simulations, we compare these methods, identify their respective advantages, and provide recommendations for adoption and implementation in environmental accountability research.