Bayesian Methods for Causal Inference in the Analysis of Power Plant Emission Controls (Chanmin Kim-BU Biostat)

Emission control technologies installed on power plant smokestacks are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution, and human health, many of these relationships have never been estimated or empirically verified amid the realities of actual regulatory implementation. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect of a particular control technology on ambient pollution is mediated through causal effects on power plant emissions. Since power plants emit various compounds that contribute to ambient pollution, we develop new statistical methods for settings with multiple intermediate mediating factors that are measured contemporaneously, may interact with one another, and may exhibit joint mediating effects. Specifically, we propose new methods leveraging two related frameworks for causal inference in the presence of mediating variables: principal stratification and causal mediation analysis. We define principal effects based on multiple mediators, and also introduce a new decomposition of the total effect of an intervention on ambient pollution into the natural direct effect and natural indirect effects for all mediating emissions jointly, each pair of emissions, and each emission individually. Both approaches are anchored to the exact same model for the observed data, which we specify with flexible Bayesian nonparametric techniques. We provide assumptions for estimating principal causal effects, then augment these with an additional assumption required to conduct a causal mediation analysis relying on natural direct and indirect effects. The principal stratification and causal mediation analyses are interpreted in tandem to provide the first comprehensive empirical investigation of the presumed causal pathways that motivate a variety of air quality regulatory policies. Further extensions will be discussed.