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Elements of External Validity: Framework, Design, and Analysis

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

March 07, 2022 (10:00 AM PST - 10:25 AM PST)
Speaker(s): Erin Hartman (University of California, Berkeley)
Location: MSRI: Online/Virtual
  • Causal inference

  • External validity

  • generalization

  • Randomized Experiment

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External validity of causal findings is a focus of long-standing debates in the social sciences. While the issue has been extensively studied at the conceptual level, in practice, few empirical studies have explicit analysis aimed towards externally valid inferences. In this article, we make three contributions to improve empirical approaches for external validity. First, we propose a formal framework that encompasses four dimensions of external validity; X-, T -, Y -, and C-validity (populations, treatments, outcomes, and contexts). The proposed framework synthesizes diverse external validity concerns. We then distinguish two goals of generalization. To conduct effect-generalization — generalizing the magnitude of causal effects — we introduce three estimators of the target population causal effects. For sign-generalization — generalizing the direction of causal effects — we propose a novel multiple-testing procedure under weaker assumptions. We illustrate our methods through field, survey, and lab experiments as well as observational studies.

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