  We develop a causal inference approach to estimate the number of adverse
health events prevented by large-scale air quality regulations via changes in
exposure to multiple pollutants. This approach is motivated by regulations that
impact pollution levels in all areas within their purview. We introduce a
causal estimand called the Total Events Avoided (TEA) by the regulation,
defined as the difference in the expected number of health events under the
no-regulation pollution exposures and the observed number of health events
under the with-regulation pollution exposures. We propose a matching method and
a machine learning method that leverage high-resolution, population-level
pollution and health data to estimate the TEA. Our approach improves upon
traditional methods for regulation health impact analyses by clarifying the
causal identifying assumptions, utilizing population-level data, minimizing
parametric assumptions, and considering the impacts of multiple pollutants
simultaneously. To reduce model-dependence, the TEA estimate captures health
impacts only for units in the data whose anticipated no-regulation features are
within the support of the observed with-regulation data, thereby providing a
conservative but data-driven assessment to complement traditional parametric
approaches. We apply these methods to investigate the health impacts of the
1990 Clean Air Act Amendments in the US Medicare population.
