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Elements of External Validity

Framework, Design, and Analysis

Bibliographic Data

ID3203489
AuthorsNaoki Egami (0000-0002-5491-2174, Columbia University, corresponding author), Elwood Hartman (0000-0003-4824-5405, University of California, Berkeley, corresponding author)
Year2023
Volume117
Issue3
Pages1070-1088
Publication date2023-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Political Science Review (JOURNAL)
Journal identifiersISSN: 0003-0554 • E-ISSN: 1537-5943
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0003055422000880
OpenAlexW4303969279
LanguageEN
Citations received45
References cited30

The external validity of causal findings is a focus of long-standing debates in the social sciences. Although the issue has been extensively studied at the conceptual level, in practice few empirical studies include an explicit analysis that is directed toward 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

Causal inference · Causal model · Econometrics · Empirical research · Estimator · External validity · Generalization · Internal validity · Observational study · Population · Sociology · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Social Psychology · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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Unique citing works45
Citations per year15
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 44

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