  Environmental epidemiologists are increasingly interested in establishing
causality between exposures and health outcomes. A popular model for causal
inference is the Rubin Causal Model (RCM), which typically seeks to estimate
the average difference in study units' potential outcomes. An important
assumption under RCM is no interference; that is, the potential outcomes of one
unit are not affected by the exposure status of other units. The no
interference assumption is violated if we expect spillover or diffusion of
exposure effects based on units' proximity to other units and several other
causal estimands arise. Air pollution epidemiology typically violates this
assumption when we expect upwind events to affect downwind or nearby locations.
This paper adapts causal assumptions from social network research to address
interference and allow estimation of both direct and spillover causal effects.
We use propensity score-based methods to estimate these effects when
considering the effects of the Environmental Protection Agency's 2005
nonattainment designations for particulate matter with aerodynamic diameter
less than 2.5 micrograms per cubic meter (PM2.5) on lung cancer incidence using
county-level data obtained from the Surveillance, Epidemiology, and End Results
(SEER) Program. We compare these methods in a rigorous simulation study that
considers both spatially autocorrelated variables, interference, and missing
confounders. We find that pruning and matching based on the propensity score
produces the highest probability coverage of the true causal effects and lower
mean squared error. When applied to the research question, we found protective
direct and spillover causal effects.
