Bayesian Causal Inference for Estimating Impacts of Air Pollution Exposure
Liao, Shirley X.
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CitationLiao, Shirley X. 2019. Bayesian Causal Inference for Estimating Impacts of Air Pollution Exposure. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractEstimation of the causal effect of air pollution exposure on population health measures poses unique challenges. One commonly used method for estimating causal effects on such data is propensity score analysis (PSA), which controls for confounding in a ``design" stage where propensity scores (PS) are estimated and implemented. Our first paper addresses uncertainty in the design stage of PSA and formulates a probability distribution for the design-stage output in order to lend a degree of formality to Bayesian methods for PSA (BPSA) that have gained attention in recent literature. A procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of design-stage outputs is then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants. In order to address seasonality in air pollution emissions, as well as time-varying confounding which occurs from weather and climate variables, our second paper extends two procedures for estimating the average treatment effect on the overlap population (ATO), which may be estimated with less bias and less variability over replications than the average treatment effect over the general population (ATE) via inverse probability weighting (IPW) or stabilize weighting (SW) when low covariate overlap exists in the data. An analysis using these methods is performed on Medicare beneficiaries residing across 18,480 zip codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease hospitalization, accounting for seasonal patterns that lead to change in treatment over time. Our third paper addresses non-linear confounding and higher-order interactions which may exist in the relationship between ozone exposure and violent criminal activity by performing an analysis using Bayesian additive regression trees (BART), a powerful machine learning procedure able to model complex, non-linear relationships. This study employs time-series data from 6 cities in the US (Chicago, NYC, Atlanta, Philadelphia, Phoenix, LA) from 2009 to 2018 in order to estimate the causal effect of ozone exposure above NAAQS standards for air quality, as well as of a continuous causal effect of ozone exposure on violent crime rates.
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