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We develop a causal inference approach to estimate the number of adversehealth events prevented by large-scale air quality regulations via changes inexposure to multiple pollutants. This approach is motivated by regulations thatimpact pollution levels in all areas within their purview. We introduce acausal estimand called the Total Events Avoided (TEA) by the regulation,defined as the difference in the expected number of health events under theno-regulation pollution exposures and the observed number of health eventsunder the with-regulation pollution exposures. We propose a matching method anda machine learning method that leverage high-resolution, population-levelpollution and health data to estimate the TEA. Our approach improves upontraditional methods for regulation health impact analyses by clarifying thecausal identifying assumptions, utilizing population-level data, minimizingparametric assumptions, and considering the impacts of multiple pollutantssimultaneously. To reduce model-dependence, the TEA estimate captures healthimpacts only for units in the data whose anticipated no-regulation features arewithin the support of the observed with-regulation data, thereby providing aconservative but data-driven assessment to complement traditional parametricapproaches. We apply these methods to investigate the health impacts of the1990 Clean Air Act Amendments in the US Medicare population.

Causal inference and machine learning approaches for evaluation of the health impacts of large-scale air quality regulations