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Assessing External Validity Over Worst-Case Subpopulations

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

March 09, 2022 (10:30 AM PST - 10:55 AM PST)
Speaker(s): Hongseok Namkoong (Columbia University)
Location: SLMath: Online/Virtual
Tags/Keywords
  • Causal inference

  • Robust Optimization

  • Semiparametric Statistics

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Assessing External Validity Over Worst-Case Subpopulations

Abstract

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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Assessing External Validity Over Worst-Case Subpopulations

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