Discovering Heterogeneous Exposure Effects in Air Pollution Studies

• Activity Number: 65 - New Methods for Identifying and Testing Heterogeneous Treatment Effects in One or a Pair of Studies
• Type: Topic Contributed
• Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Kwonsang Lee* and Dylan Small and Francesca Dominici
• Companies: Harvard University and University of Pennsylvania and Harvard T.H. Chan School of Public Health
• Keywords: Causal effect; Causal inference ; Observational study; Recursive partitioning; Sample split; Unmeasured confounding

Abstract

Studies have shown that exposure to air pollution, even at low levels, significantly increases mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to reduce air pollution exposure is needed. In this paper, we introduce a novel statistical method that splits the data into two subsamples: (a) Using the first subsample, we consider a data-driven search for de novo discovery of subgroups that could have exposure effects that differ from the population mean; and then (b) using the second subsample, we quantify evidence of effect modification among the subgroups with nonparametric randomization-based tests. We also develop a sensitivity analysis method to assess the robustness of the conclusions to unmeasured confounding bias. Via simulation studies and theoretical arguments, we demonstrate that the de novo method can substantially increase the statistical power of the test. We apply our method to the data of 1,612,414 Medicare beneficiaries in New England region in the United States for the period 2000 to 2006.