Leveraging Observational Outcomes to Improve the Generalizability of Experimental Results

• Activity Number: 534 - Tradeoff Between Risks and Benefits When Transporting Model Under Distribution Shift
• Type: Invited
• Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Melody Huang* and Naoki Egami and Erin Hartman and Luke Miratrix
• Companies: University of California, Berkeley and Columbia University and University of California, Berkeley and Harvard University
• Keywords: causal inference; generalizability; data fusion; transportability

Abstract

Randomized control trials are often considered the gold standard in causal inference due to their high internal validity. Despite its importance, generalizing experimental results to a target population is challenging in social and biomedical sciences. Recent papers clarify assumptions necessary for generalization and develop various weighting estimators for the population average treatment effect (PATE). However, in practice, many of these methods result in large variance and little statistical power, thereby limiting the value of the PATE inference. In this article, we propose post-residualized weighting, in which information about the outcome measured in the observational population data is used to improve the efficiency of many existing popular methods without making additional assumptions. We empirically demonstrate the efficiency gains through simulations and apply our proposed method to a set of jobs training experiments.