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September 2019 Bayesian methods for multiple mediators: Relating principal stratification and causal mediation in the analysis of power plant emission controls
Chanmin Kim, Michael J. Daniels, Joseph W. Hogan, Christine Choirat, Corwin M. Zigler
Ann. Appl. Stat. 13(3): 1927-1956 (September 2019). DOI: 10.1214/19-AOAS1260

Abstract

Emission control technologies installed on power plants 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 empirically verified. 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 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 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 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.

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Chanmin Kim. Michael J. Daniels. Joseph W. Hogan. Christine Choirat. Corwin M. Zigler. "Bayesian methods for multiple mediators: Relating principal stratification and causal mediation in the analysis of power plant emission controls." Ann. Appl. Stat. 13 (3) 1927 - 1956, September 2019. https://doi.org/10.1214/19-AOAS1260

Information

Received: 1 July 2017; Revised: 1 January 2019; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145980
MathSciNet: MR4019162
Digital Object Identifier: 10.1214/19-AOAS1260

Keywords: Ambient $\mathrm{PM}_{2.5}$ , Bayesian nonparametrics , Gaussian copula , multipollutants , natural indirect effect

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 3 • September 2019
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