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Abstract:
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Causal inference for environmental data is often challenging due to interference: outcomes for observational units depend on some combination of local and nonlocal treatment. 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; 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, 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 interference structure. To illustrate the usefulness of our methodology, we analyze the effectiveness of air quality interventions at pollution sources (such as coal-fired power plants) on the prevalence of asthma hospitalizations in Texas.
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