arXiv 2 May 2023 · Econometrics · publishedJournal of Econometrics (2026) · 1 citations (OpenAlex)
arXiv:2305.01435 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites"/“contexts"). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental" sites in which an intervention has already been implemented to a new “target" site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the baseline as functional data, and this choice is motivated by the observation that unobserved site-specific confounders manifest themselves not only in average levels of outcomes, but also how these interact with observed unit-specific attributes. We consider the problem of determining the optimal finite-dimensional feature space in which to solve that prediction problem. Our approach is design-based in the sense that the performance of the predictor is evaluated given the specific, finite selection of experimental and target sites. Our approach is nonparametric, and our formal results concern the construction of an optimal basis of predictors as well as convergence rates for the estimated conditional average treatment effect relative to the constrained-optimal population predictor for the target site. We quantify the potential gains from adapting experimental estimates to a target location in an application to conditional cash transfer (CCT) programs using a combined data set from five multi-site randomized controlled trials.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Benatia, Carrasco, and Florens (2017) Functional Linear Regression wtih Functional Response | 0.928 | 4 | 3 | 100% |
| 2 | Ramsay and Silverman (2005) Functional Data Analysis (2nd ed.) | 0.928 | 4 | 3 | 100% |
| 3 | Dehejia, Pop-Eleches, and Samii (2021) From Local to Global: External Validity in a Fertility Natural Experiment | 0.874 | 5 | 2 | 100% |
| 4 | Hotz, Imbens, and Mortimer (2005) Predicting the Efficacy of Future Training Programs using Past Experiences at other Locations | 0.874 | 5 | 2 | 100% |
| 5 | Nie, Imbens, and Wager (2021) Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations | 0.874 | 5 | 2 | 100% |
| 6 | He, Müller, Wang, and Yang (2010) Functional Linear Regression via Canonical Analysis | 0.855 | 8 | 4 | 62% |
| 7 | Barber, Candès, Ramdas, and Tibshirani (2023) Conformal Prediction beyond Exchangeability | 0.843 | 3 | 3 | 100% |
| 8 | Lei, G'Sell, Rinaldo, Tibshirani, and Wasserman (2018) Distribution-Free Predictive Inference for Regression | 0.843 | 3 | 3 | 100% |
| 9 | Vovk, Gammerman, and Shafer (2005) Algorithmic Learning in a Random World | 0.843 | 3 | 3 | 100% |
| 10 | Gechter (2023) Generalizing the Results from Social Experiments: Theory and Evidence from India | 0.811 | 4 | 2 | 100% |
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