Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning
利用因果推断与机器学习评估1990年《清洁空气法》修正案的健康影响
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…