Asthma and air pollution: Estimating direct and indirect effects of power plant interventions on asthma-related ED visits with a probabilistic exposure model.

Causal inference for environmental health data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and nonlocal treatment (Zigler and Papadogeorgou 2021, Zigler et al. 2020, Reich et al. 2021). This is especially relevant when analyzing the effectiveness of air quality interventions at pollution sources (such as coal-fired power plants) on human health, as air pollution exposure is affected by upwind pollution sources, regional differences in demographics, and meteorologic processes. Consequently, the analysis and design of regulatory policies intended to improve public health stands to benefit from causal methods which account for complex sources of treatment interference. In recent years, methods for causal inference with general interference have included the specification of an exposure model, in which treatment assignments are mapped to an exposure value (Aronow and Samii 2017, Karwa and Airoldi 2018, Forastiere et al. 2021); causal estimands of the direct and indirect (i.e., 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 (Aronow and Samii 2017, Forastiere et al. 2021). However, in environmental settings, treatment interference is often 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 mapping and marginalize estimates of causal effects over uncertainty in the structure of interference. To illustrate the usefulness of our methodology, we analyze the effectiveness of air quality interventions at coal-fired power plants on the prevalence of asthma-related emergency department (ED) visits in Texas. In particular, treatment assignments are mapped to exposure levels via a mechanistic model of air pollution transport (Wikle et al. 2020), and causal estimands are defined to accommodate the corresponding uncertainty in the estimated exposure. We use our results to identify individual upwind power plants that should be targeted for future regulatory intervention, and discuss the relevance of this work to the study of environmental health data at large. This research

causal inference

air pollution

asthma

treatment interference (spillover)

spatio-temporal statistics

mechanistic modeling