A0278
Title: Causal health impacts of power plant emission controls under modeled and uncertain physical process interference
Authors: Corwin Zigler - Brown University (United States) [presenting]
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 nonlocal 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. The goal is to 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. Methods are developed for causal inference with interference when the underlying network structure is not known with certainty and, instead, must be estimated from ancillary data. Notably, uncertainty in the interference structure is propagated to the resulting causal effect estimates. A Bayesian, spatial mechanistic model is offered for the interference mapping, which is combined with a flexible nonparametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. The 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.
