A Causal Exposure Response Function with Local Adjustment for Confounding
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In the last two decades, ambient levels of air pollution have declined substantially. Yet, as mandated by the Clean Air Act, we must continue to address the following question: is exposure to levels of air pollution that are well below the National Ambient Air Quality Standards (NAAQS) harmful to human health? Furthermore, the highly contentious nature surrounding environmental regulations necessitates casting this question within a causal inference framework. Several parametric and semi-parametric regression modeling approaches have been developed for estimating the exposure-response (ER) curve. However, most of these approaches: 1) are not formulated in the context of a potential outcome framework for causal inference; 2) adjust for the same set of potential confounders across all levels of exposure; and 3) do not account for model uncertainty regarding covariate selection and shape of the ER. In this paper, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment). LERCA allows for: a) different confounders and different strength of confounding at the different exposure levels; and b) model uncertainty regarding confounders' selection and the shape of ER. Also, LERCA provides a principled way of assessing the observed covariates' confounding importance at different exposure levels. We compare our proposed method with state of the art approaches in causal inference for ER estimation using simulation studies. We also apply the proposed method to a large data set that includes health, weather, demographic, and pollution for 5,362 zip codes and for the years of 2011-2013. An R package is available at https://github.com/gpapadog/LERCA.
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