  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. Notably, uncertainty in the interference
structure is propagated to the resulting causal effect estimates. 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.
