ArticleThe annals of applied statistics2019
BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS.
Article in The annals of applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Post-hoc mediation analysis of two biomarkers, and survival in acute respiratory distress syndrome.Scientific reports · 2025Trial
- Trial
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Accounting for the Health Benefits of Air Pollution Regulations in China, 2008-2019.Research report (Health Effects Institute) · 2026Article
- A sensitivity analysis approach to principal stratification with a continuous longitudinal intermediate outcome: applications to a cohort stepped wedge trial.Biostatistics (Oxford, England) · 2026Article
- Bayesian nonparametric trees for principal causal effects.Biometrics · 2025Article
- High-dimensional Bayesian mediation analysis with adaptive Laplace priors.Statistics and its interface · 2025Article
- Bayesian Mediation Analysis for Time-to-Event Outcome: Investigating Racial Disparity in Breast Cancer Survival.Communications in statistics: theory and methods · 2025Article
- Path-specific causal decomposition analysis with multiple correlated mediator variables.Statistics in medicine · 2024Article
- Policy-induced air pollution health disparities: Statistical and data science considerations.Science (New York, N.Y.) · 2024Review
- A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.Biostatistics (Oxford, England) · 2024Article
- A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.Biometrics · 2024Article
- Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior.Journal of agricultural, biological, and environmental statistics · 2023Article
- Bayesian mediation analysis methods to explore racial/ethnic disparities in anxiety among cancer survivors.Behaviormetrika · 2023Article
- Bayesian network mediation analysis with application to the brain functional connectome.Statistics in medicine · 2022Article
- Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures.American journal of epidemiology · 2021Article
- Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Measuring rater bias in diagnostic tests with ordinal ratings.Statistics in medicine · 2021Article
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5 authors.
Funding
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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