Elements of External Validity: Framework, Design, and Analysis

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.