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Activity Number: 320 - Electronic Health Records, Causal Inference and Miscellaneous
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #319105
Title: Sensitivity Analysis in the Generalization of Experimental Results
Author(s): Melody Huang*
Companies: UCLA
Keywords: sensitivity analysis; generalization; RCT; weighting

Randomized control trials (RCT’s) allow researchers to estimate causal effects in an experimental sample with minimal identifying assumptions. However, to generalize a causal effect from an RCT to a target population, researchers must adjust for a set of treatment effect moderators. In practice, it is impossible to know whether the set of moderators has been properly accounted for. In the following paper, I propose a three parameter sensitivity analysis for the generalization of experimental results using weighted estimators, with several advantages over existing methods. First, the framework does not require any parametric assumption on either the selection or treatment effect heterogeneity mechanism. Second, I show that the sensitivity parameters are guaranteed to be bounded and propose (1) a diagnostic for researchers to determine how robust a point estimate is to killer confounders, and (2) an adjusted calibration approach for researchers to accurately benchmark the parameters using existing data. Finally, I demonstrate that the proposed framework can be easily extended to the class of doubly robust, augmented weighted estimators.

Authors who are presenting talks have a * after their name.

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