Emily Breza, Arun G. Chandrasekhar, Davide Viviano
arXiv 23 Jan 2025 · Econometrics
arXiv:2501.13355 · PDF · DOI · OpenAlex · Extracted main text
When studying policy interventions, researchers often pursue two goals: i) identifying for whom the program has the largest effects (heterogeneity) and ii) determining whether those patterns of treatment effects have predictive power across environments (generalizability). We develop a framework to learn when and how to partition observations into groups of individual and environmental characterstics within which treatment effects are predictively stable, and when instead extrapolation is unwarranted and further evidence is needed. Our procedure determines in which contexts effects are generalizable and when, instead, researchers should admit ignorance and collect more data. We provide a decision-theoretic foundation, derive finite-sample regret guarantees, and establish asymptotic inference results. We illustrate the benefits of our approach by reanalyzing a multifaceted anti-poverty program across six countries.
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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 | Banerjee, A., E. Duflo, N. Goldberg, D. Karlan, R. Osei, W. Parienté… (2015) A multifaceted program causes lasting progress for the very poor: Evidence from six countries | 1.000 | 8 | 4 | 100% |
| 2 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 5 | 3 | 100% |
| 3 | Bonhomme, S. and E. Manresa (2015) Grouped patterns of heterogeneity in panel data | 0.928 | 4 | 3 | 100% |
| 4 | Venkateswaran, A., A. Sankar, A. G. Chandrasekhar, and T. H. McCormick (2024) Robustly estimating heterogeneity in factorial data using rashomon partitions | 0.928 | 4 | 3 | 100% |
| 5 | Wager, S. and S. Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.928 | 4 | 3 | 100% |
| 6 | Gechter, M., K. Hirano, J. Lee, M. Mahmud, O. Mondal, J. Morduch, S.… (2024) Selecting experimental sites for external validity | 0.843 | 3 | 3 | 100% |
| 7 | Chernozhukov, V., M. Demirer, E. Duflo, and I. Fernandez-Val (2018) Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immuniza… | 0.811 | 4 | 2 | 100% |
| 8 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.737 | 4 | 3 | 50% |
| 9 | Broderick, T., R. Giordano, and R. Meager (2020) An automatic finite-sample robustness metric: when can dropping a little data make a big difference? | 0.644 | 2 | 2 | 100% |
| 10 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.644 | 2 | 2 | 100% |
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