A1257
Title: Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
Authors: Suyeon Kang - University of Central Florida (United States) [presenting]
Alexander Franks - UC Santa Barbara (United States)
Michelle Audirac - Harvard TH Chan School of Public Health (United States)
Danielle Braun - Harvard TH Chan School of Public Health (United States)
Joseph Antonelli - University of Florida (United States)
Abstract: A framework is developed for partial identification of causal effects in settings with multiple treatments and multiple outcomes. We highlight several advantages of jointly analyzing causal effects across multiple estimands under a "factor confounding assumption" where residual dependence amongst treatments and outcomes is assumed to be driven by unmeasured confounding. In this setting, we show that joint partial identification regions for multiple estimands can be more informative than considering partial identification for individual estimands one at a time. We additionally show how assumptions related to the strength of confounding or the magnitude of plausible effect sizes for any one estimand can reduce the partial identification regions for other estimands. As a special case of this result, we explore how negative control assumptions reduce partial identification regions and discuss conditions under which point identification can be obtained. We develop novel computational approaches to finding partial identification regions under a variety of these assumptions. Lastly, we demonstrate our approach in an analysis of the causal effects of multiple air pollutants on several health outcomes in the United States using claims data from Medicare, where we find that some exposures have effects that are robust to the presence of unmeasured confounding.
