Assessing External Validity in Practice

38 Pages Posted: 29 Aug 2022 Last revised: 4 Jun 2023

See all articles by Sebastian Galiani

Sebastian Galiani

University of Maryland - Department of Economics

Brian Quistorff

Office of the Chief Economist

Date Written: August 18, 2022

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).

Suggested Citation

Galiani, Sebastian and Quistorff, Brian, Assessing External Validity in Practice (August 18, 2022). Available at SSRN: https://ssrn.com/abstract=4193276 or http://dx.doi.org/10.2139/ssrn.4193276

Sebastian Galiani (Contact Author)

University of Maryland - Department of Economics ( email )

College Park, MD 20742
United States

Brian Quistorff

Office of the Chief Economist ( email )

1441 L Street NW
Washington, DC 20910
United States

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