Papers › Assessing External Validity Over Worst-case Subpopulations

Assessing External Validity Over Worst-case Subpopulations

5 Jul 2020arXiv:2007.02411archive 2025-07-28

Sookyo Jeong, Hongseok Namkoong

Study populations are typically sampled from limited points in space and time, and marginalized groups are underrepresented. To assess the external validity of randomized and observational studies, we propose and evaluate the worst-case treatment effect (WTE) across all subpopulations of a given size, which guarantees positive findings remain valid over subpopulations. We develop a semiparametrically efficient estimator for the WTE that analyzes the external validity of the augmented inverse propensity weighted estimator for the average treatment effect. Our cross-fitting procedure leverages flexible nonparametric and machine learning-based estimates of nuisance parameters and is a regular root-n estimator even when nuisance estimates converge more slowly. On real examples where external validity is of core concern, our proposed framework guards against brittle findings that are invalidated by unanticipated population shifts.

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cutSample sookyojeong/worst-ate/code/calculate.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 85a3a09412846a89 · report
geth sookyojeong/worst-ate/code/calculate.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 651bdd4788c2fb2e · report
wte sookyojeong/worst-ate/code/calculate.py official repository unverified no licence file found · pointer only · 0c19513a405d9a28 · report

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