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A0595
Title: Bayesian methods for multiple mediators: Relating principal stratification and causal mediation Authors:  Chanmin Kim - SungKyunKwan University (Korea, South) [presenting]
Michael Daniels - University of Florida (United States)
Joseph Hogan - Brown University School of Public Health (United States)
Christine Choirat - SDSC (Switzerland)
Corwin Zigler - University of Texas at Austin (United States)
Abstract: The goal is to develop new statistical methods to quantify relationships between emissions, ambient air pollution, and human health. We frame evaluation as a mediation analysis to assess 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 methods for multiple intermediate variables 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 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 combinations of mediators. Both approaches are anchored to the same observed-data models, which we specify with Bayesian nonparametric techniques. We provide assumptions for estimating principal causal effects, then augment these with an additional assumption required for causal mediation analysis. The two analyses, interpreted in tandem, provide the first empirical investigation of the presumed causal pathways that motivate important air quality regulatory policies.