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

TL;DR

This paper introduces a causal inference framework using matching and machine learning to estimate health benefits of air quality regulations, focusing on the total adverse events prevented across populations.

Contribution

It presents a novel causal estimand, the Total Events Avoided (TEA), and methods that improve health impact assessments by reducing assumptions and accounting for multiple pollutants.

Findings

Estimated health events prevented by the 1990 Clean Air Act Amendments.

Methodologically advances in causal inference for environmental health policies.

Provides conservative, data-driven estimates of regulation impacts.

Abstract

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.