Victor Chernozhukov, Sokbae Lee, Adam M. Rosen, Liyang Sun
arXiv 15 Feb 2025 · Econometrics
arXiv:2502.10653 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a framework for selecting policies that maximize expected welfare under estimation uncertainty. The proposed method explicitly balances the size of the estimated welfare against the uncertainty inherent in its estimation, ensuring that chosen policies meet a reporting guarantee, namely, that actual welfare is guaranteed not to fall below the reported estimate with a pre-specified confidence level. We produce the efficient decision frontier, describing policies that offer maximum estimated welfare for a given acceptable level of estimation risk. We apply this approach to a variety of settings, including the selection of policy rules that allocate individuals to treatments and the allocation of limited budgets among competing social programs.
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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 | Chernozhukov, Lee and Rosen (2013) Intersection Bounds: Estimation and Inference self | 0.928 | 4 | 3 | 100% |
| 2 | Manski (2021) Econometrics for Decision Making: Building on Foundations Sketched by Wald and Haavelmo | 0.874 | 5 | 2 | 100% |
| 3 | Athey and Wager (2021) Policy Learning with Observational Data | 0.811 | 4 | 2 | 100% |
| 4 | Kitagawa and Tetenov (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.811 | 4 | 2 | 100% |
| 5 | Andrews and Chen (2025) Certified Decisions | 0.811 | 4 | 2 | 100% |
| 6 | Ben-Michael, Greiner, Imai and Jiang (2025) Safe Policy Learning through Extrapolation: Application to Pre-Trial Risk Assessment | 0.737 | 3 | 2 | 100% |
| 7 | Chernozhukov, Chetverikov and Koike (2023) Nearly optimal central limit theorem and bootstrap approximations in high dimensions | 0.644 | 2 | 2 | 100% |
| 8 | Chernozhukov, Chetverikov, Kato and Koike (2022) Improved central limit theorem and bootstrap approximations in high dimensions | 0.644 | 2 | 2 | 100% |
| 9 | Chernozhukov, Chetverikov, Kato and Koike (2023) High-dimensional data bootstrap | 0.644 | 2 | 2 | 100% |
| 10 | Andrews, Kitagawa and McCloskey (2024) Inference on winners | 0.644 | 2 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.