Elements of External Validity: Framework, Design, and Analysis

Abstract

External validity of randomized experiments 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 (units, treatments, outcomes, and contexts). The proposed framework synthesizes diverse external validity concerns that arise in practice. 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 — assessing whether the direction of causal effects is generalizable, we propose a novel multiple-testing procedure under weaker assumptions. We illustrate our methods through three applications covering field, survey, and lab experiments.

Keywords: Causal inference, External validity, Generalization, Randomized Experiment