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

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

SUBMITTER: Nethery RC 

PROVIDER: S-EPMC7788006 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature