arXiv 19 Feb 2025 · Econometrics
arXiv:2502.13868 · PDF · DOI · OpenAlex · Extracted main text
Policy makers need to decide whether to treat or not to treat heterogeneous individuals. The optimal treatment choice depends on the welfare function that the policy maker has in mind and it is referred to as the policy learning problem. I study a general setting for policy learning with semiparametric Social Welfare Functions (SWFs) that can be estimated by locally robust/orthogonal moments based on U-statistics. This rich class of SWFs substantially expands the setting in Athey and Wager (2021) and accommodates a wider range of distributional preferences. Three main applications of the general theory motivate the paper: (i) Inequality aware SWFs, (ii) Inequality of Opportunity aware SWFs and (iii) Intergenerational Mobility SWFs. I use the Panel Study of Income Dynamics (PSID) to assess the effect of attending preschool on adult earnings and estimate optimal policy rules based on parental years of education and parental income.
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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 | Athey, S. and S. Wager (2021) Policy learning with observational data | 1.000 | 9 | 5 | 100% |
| 2 | Escanciano, J. C. and J. R. Terschuur (2023) Machine Learning Inference on Inequality of Opportunity self | 0.822 | 9 | 4 | 56% |
| 3 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation | 0.737 | 3 | 3 | 67% |
| 4 | Hoeffding, W (1963) Probability Inequalities for Sums of Bounded Random Variables | 0.737 | 3 | 3 | 67% |
| 5 | Zhou, Z., S. Athey, and S. Wager (2023) Offline multi-action policy learning: Generalization and optimization | 0.644 | 4 | 2 | 50% |
| 6 | Chetty, R., N. Hendren, P. Kline, and E. Saez (2014) Where is the land of opportunity? The geography of intergenerational mobility in the United States | 0.644 | 2 | 2 | 100% |
| 7 | Fort, M., A. Ichino, and G. Zanella (2020) Cognitive and noncognitive costs of day care at age 0–2 for children in advantaged families | 0.644 | 2 | 2 | 100% |
| 8 | Kitagawa, T., M. Nybom, and J. Stuhler (2018) Measurement error and rank correlations, Tech | 0.644 | 2 | 2 | 100% |
| 9 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.644 | 2 | 2 | 100% |
| 10 | Leqi, L. and E. H. Kennedy (2021) Median optimal treatment regimes | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2606.01659 | 0.511 | 2 | 1 |
| 2 | Debiased Machine Learning U-Statistics | 0.405 | 1 | 1 |
| 3 | On the Lower Confidence Band for the Optimal Welfare in Policy Learning | 0.405 | 1 | 1 |
| 4 | Leave No One Undermined: Policy Targeting with Regret Aversion | 0.405 | 1 | 1 |