Working Paper: NBER ID: w30398
Authors: Sebastian Galiani; Brian Quistorff
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: No keywords provided
JEL Codes: C55
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| propensity score reweighting (C24) | differences in average treatment effects (ATE) (C22) |
| propensity score reweighting (C24) | treatment effects vary based on observable characteristics (C21) |
| group lasso method (C51) | estimates conditional average treatment effects (CATE) more effectively (C22) |
| group lasso method (C51) | extrapolating effects to new settings (C51) |
| average treatment effects (ATE) (C22) | vary across multiple countries (O57) |
| upgrading slum housing (R28) | quality of life improvements (I31) |