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Activity Number: 151 - #LeadwithStatistics in the Social Sciences
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329178 Presentation
Title: Covariate Selection for Generalizing Experimental Results
Author(s): Erin Hartman* and Naoki Egami
Companies: UCLA and Princeton University
Keywords: generalizability; causal inference; experiments; extrapolation

Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Previous studies have shown that an unbiased estimate for the population ATE can be obtained if selection into the experiment is independent of treatment heterogeneity given a set of variables researchers adjust for. Although this separating set has simple mathematical representation, it is often unclear how to select this set in practice. In this paper, we propose a data-driven method to estimate the minimum separating set. Our approach has two advantages. First, because we find a separating set of the smallest size, it is easier for researchers to measure it in the target population. Second, our algorithm can incorporate researcher-specific data constraints. When they know certain variables are unmeasurable in the target population, our method can identify a minimal separating set subject to such constraints, if one is feasible. We validate our proposed method using naturalistic simulations.

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

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