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Causal inference with spatial environmental data is often challenging due tothe presence of interference: outcomes for observational units depend on somecombination of local and non-local treatment. This is especially relevant whenestimating the effect of power plant emissions controls on population health,as pollution exposure is dictated by (i) the location of point-sourceemissions, as well as (ii) the transport of pollutants across space via dynamicphysical-chemical processes. In this work, we estimate the effectiveness of airquality interventions at coal-fired power plants in reducing two adverse healthoutcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-causemortality. We develop methods for causal inference with interference when theunderlying network structure is not known with certainty and instead must beestimated from ancillary data. We offer a Bayesian, spatial mechanistic modelfor the interference mapping which we combine with a flexible non-parametricoutcome model to marginalize estimates of causal effects over uncertainty inthe structure of interference. Our analysis finds some evidence that emissionscontrols at upwind power plants reduce asthma ED visits and all-causemortality, however accounting for uncertainty in the interference renders theresults largely inconclusive.

Causal health impacts of power plant emission controls under modeled and uncertain physical process interference