Corwin Zigler: Bayesian Causal Inference with Uncertain Physical Process Interference

Speaker: Corwin Zigler

 

Institution: Brown University

 

Title: Bayesian Causal Inference with Uncertain Physical Process Interference

 

Abstract: Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and non-local treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by (i) the location of point-source emissions, as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work, we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. We offer a Bayesian, spatial mechanistic model for the interference mapping which we combine with a flexible non-parametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. Our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality, however accounting for uncertainty in the interference renders the results largely inconclusive.

 

Bio: Corwin Zigler is Professor of Biostatistics at the Brown University School of Public Health.  His research focuses on quantitative methodology for evaluating the health impacts of environmental and climate-related exposures.  His statistical research falls mostly in the area of causal inference, with particular focus on spatially-indexed data, spatial confounding, interference networks, physical process modeling, and Bayesian methodology. His work integrates statistical methodology, epidemiology, large-scale computation, and atmospheric science towards better understanding of how environmental and climate policies impact human health.  His research has been funded by the National Institutes of Health, the Health Effects Institute, and the U.S. Environmental Protection Agency, and he has served on multiple specialty panels for the U.S. EPA Clean Air Scientific Advisory Committee. Before arriving at Brown in 2024, Dr. Zigler served as faculty in the Department of Statistics and Data Sciences at the Unviersity of Texas at Austin and in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health.  He completed his Ph.D. in Biostatistics at the University of California, Los Angeles in 2010.