Causal Inference for Environmental Health Data: Estimating Causal Effects in the Presence of Spatial Interference
Presented by Nathan Wikle, Department of Statistics and Data Sciences, University of Texas at Austin
Wednesday, Jan 18 2023
3:30 PM-4:30 PM ET
Webex Meeting Link
Causal inference for environmental health data is often challenging due to the presence of spatial interference: outcomes for observational units depend on some combination of local and nonlocal treatment. One common solution includes the specification of an exposure model, in which treatment assignments are mapped to an exposure value; causal estimands of the local and spillover effects of treatment are defined through contrasts of the local treatment assignment and the exposure value. Notably, the exposure model is often defined via a network structure, which is assumed to be fixed and known a priori. However, in environmental settings, spatial interference may be dictated by complex, mechanistic processes that are both stochastic and poorly represented by a network. In this work, we develop methods for causal inference with interference when deterministic exposure models cannot be assumed or are unknown. We offer a Bayesian model for the interference structure which, when combined with a flexible nonparametric outcome model, allows us to marginalize estimates of causal effects over uncertainty in the interference structure. The interference structure is estimated from environmental data using a mechanistic model for spatial data. To illustrate our methodology, we analyze the effectiveness of air quality interventions in reducing two adverse health outcomes in Texas --- asthma ED visits and Medicare all-cause mortality.