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. 2020 Sep 16:1:1-12.
doi: 10.1080/01621459.2020.1803883.

Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning

Rachel C Nethery  1 , Fabrizia Mealli  2 , Jason D Sacks  3 , Francesca Dominici  1
Affiliations

Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning

Rachel C Nethery et al. J Am Stat Assoc. .

Abstract

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, utilizing population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the US Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure.

Keywords: 1990 Clean Air Act Amendments; Bayesian Additive Regression Trees; Counterfactual Pollution Exposures; Matching.

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Figures

Figure 1:
Figure 1:
Maps of estimated year-2000 zipcode level factual and counterfactual annual average PM2.5 (μg/m3) and warm season average O3 (ppb). The factual pollutant values are estimates of the true exposures with the CAAA. The counterfactual values reflect the anticipated pollutant exposures under the emissions scenario expected without the CAAA.
Figure 2:
Figure 2:
Primary analysis (PA) and sensitivity analysis (SA) estimates and 95% CIs for the TEA in the Medicare population in 2000 and 2001 due CAAA-attributable changes in PM2.5 and O3 in the year 2000. Estimation is performed using matching with a GAM bias correction, BART, and Poisson regression (PR).
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