Estimating Causal Effects of Air Quality Regulations Using Principal Stratification for Spatially Correlated Multivariate Intermediate Outcomes

• Activity Number: 304
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Corwin Zigler*+
• Keywords: Principal Stratification ; Causal Inference ; Interference ; Spatial Data ; Bayesian Statistics ; Air Pollution

Abstract

Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because 1) changes in air quality are intermediates on the causal pathway between regulation and health; 2) regulations typically affect multiple pollutants on the causal pathway towards health; and 3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal-stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously-scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.