Assessing external validity in practice

Sebastian Galiani and Brian Quistorff

Research in Economics, 2024, vol. 78, issue 3

Abstract: We review, from a practical standpoint, the evolving literature on assessing external validity (EV) of estimated treatment effects. We review existing EV measures, and focus on methods that permit multiple datasets (Hotz et al., 2005). We outline criteria for practical usage, evaluate the existing approaches, and identify a gap in potential methods. Our practical considerations motivate a novel method utilizing the Group Lasso (Yuan and Lin, 2006) to estimate a tractable regression-based model of the conditional average treatment effect (CATE). This approach can perform better when settings have differing covariate distributions and allows for easily extrapolating the average treatment effect to new settings. We apply these measures to a set of identical field experiments upgrading slum dwellings in three different countries (Galiani et al., 2017).

Keywords: Conditional average treatment effect; External validity; Machine learning; Randomized controlled trials (search for similar items in EconPapers)
Date: 2024
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Working Paper: Assessing External Validity in Practice (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reecon:v:78:y:2024:i:3:s1090944324000280

DOI: 10.1016/j.rie.2024.100964

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