Evaluating Air Pollution Policies using Balancing Weights

3353 

Contributed Abstract 

Paper 

Kevin Josey (1), Mauricio Tec (2), Rachel Nethery (2)

(1) University of Colorado, Aurora, CO, (2) Harvard University, Boston, MA

Mauricio Tec  
Harvard University

Rachel Nethery  
Harvard University

Kevin Josey  
University of Colorado

Kevin Josey  
Harvard University

The association between long-term exposure to fine particulate matter (PM2.5) and the risk of various health outcomes such as mortality and major adverse cardiovascular events has been extensively documented over the past several decades. However, these previous studies often lack comprehensive evaluations regarding how a proposed policy implementation might influence this prevalent public health concern. In response, we propose a balancing weight framework to estimate and assess counterfactual outcomes under the assumption that the distribution of exposures has been shifted through policy interventions. The weights utilized for evaluating these counterfactual outcomes are designed to optimally balance the moments and correlations of the covariates with the factual exposures, and match them with those derived from the shifted counterfactual exposures. To illustrate the applicability of our method, we evaluate the potential impact of several policy interventions to PM2.5 on lowering the incidence of major adverse cardiovascular events in a randomly selected 10% cohort of high-risk Medicare recipients. We support our application with a numerical study of the proposed methodology.

Causal Inference|Stochastic Interventions|Calibration Weights|Air Pollution Epidemiology|Cardiovascular Disease|

Section on Statistics and the Environment

Environmental Policy and Regulations