Centers of Excellence


Center for Causal Inference

New working paper on longitudinal mediation

New working paper on longitudinal mediation

Mar 1, 2019

Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions

Chanmin Kim, Corwin M Zigler, Michael J Daniels, Christine Choirat, and Jason A Roy

Pollutant emissions from coal-burning power plants have been deemed to adversely impact ambient air quality and public health conditions. Despite the noticeable reduction in emissions and the improvement of air quality since the Clean Air Act (CAA) became the law, the public-health benefits from changes in emissions have not been widely evaluated yet. In terms of the chain of accountability (HEI Accountability Working Group, 2003), the link between pollutant emissions from the power plants (SO2) and public health conditions (respiratory diseases) accounting for changes in ambient air quality (PM2.5) is unknown. We provide the first assessment of the longitudinal effect of specific pollutant emission (SO2) on public health outcomes that is mediated through changes in the ambient air quality. It is of particular interest to examine the extent to which the effect that is mediated through changes in local ambient air quality differs from year to year. In this paper, we propose a Bayesian approach to estimate novel causal estimands: time-varying mediation effects in the presence of mediators and responses measured every year. We replace the commonly invoked sequential ignorability assumption with a new set of assumptions which are sufficient to identify the distributions of the natural indirect and direct effects in this setting.

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About CCI

The Center for Causal Inference (CCI) is a research center that is operating under a partnership between Penn’s Center for Clinical Epidemiology and Biostatistics (CCEB), the Department of Biostatistics and Epidemiology, Rutgers School of Public Health, and Penn’s Wharton School. The mission of the CCI is to be a leading center for research and training in the development and application of causal inference theory and methods.

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