Evaluating Air Pollution Policies using Balancing Weights

Oregon Convention Center 

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