Bayesian Causal Inference with Uncertain Physical Process Interference, by Corwin Zigler (Brown University)

November 2, 2026

Title: Bayesian Causal Inference with Uncertain Physical Process Interference

Speaker: Corwin Zigler, Professor at Brown University.

Time: Friday, November 6, 10:00 AM,  Austin 326.

Link: https://teams.microsoft.com/meet/210569911634778?p=Jo739SHWl5ZtUQBKJX

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 of Corwin Zigler: 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.