Estimating Longitudinal Causal Effects of Air Pollution Exposures Using Marginal Structural Models

• Activity Number: 235 - Recent Advancements in Nonparametric and Semiparametric Methodologies and Their Applications
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Daniel Malinsky*
• Companies: Columbia University
• Keywords: Causal inference; Air pollution; Environmental health; Causality; Time-varying confounding; Inverse probability weighting

Abstract

This talk will present some ongoing work-in-progress on estimating the causal effects of air pollution exposures on chronic lung disease outcomes using marginal structural models (MSMs). The data comes from a multi-site cohort longitudinal study that has been collecting data for more than 20 years on a diverse group of older American adults, with multiple outcomes measured including lung density (emphysema damage) by CT scan. Participants have been linked to ambient air pollution exposure levels on several pollutants of interest – we focus on ozone, NOx, and PM2.5. We will discuss challenges and approaches to estimating effects of these continuous exposures while accounting for time-varying confounding in the MSM framework.