Yanqin Fan, Yuan Qi, Gaoqian Xu
arXiv 1 May 2025 · Econometrics
arXiv:2505.00256 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes an optimal policy that targets the average welfare of the worst-off $\alpha$-fraction of the post-treatment outcome distribution. We refer to this policy as the $\alpha$-Expected Welfare Maximization ($\alpha$-EWM) rule, where $\alpha \in (0,1]$ denotes the size of the subpopulation of interest. The $\alpha$-EWM rule interpolates between the expected welfare ($\alpha=1$) and the Rawlsian welfare ($\alpha\rightarrow 0$). For $\alpha\in (0,1)$, an $\alpha$-EWM rule can be interpreted as a distributionally robust EWM rule that allows the target population to have a different distribution than the study population. Using the dual formulation of our $\alpha$-expected welfare function, we propose a debiased estimator for the optimal policy and establish its asymptotic upper regret bounds. In addition, we develop asymptotically valid inference for the optimal welfare based on the proposed debiased estimator. We examine the finite sample performance of the debiased estimator and inference via both real and synthetic data.
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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 | Kitagawa, T. and Tetenov, A (2021) Equality-minded treatment choice | 1.000 | 14 | 5 | 100% |
| 2 | Luedtke, A. R. and van der Laan, M. J (2016) Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy | 1.000 | 6 | 3 | 100% |
| 3 | Wang, L., Zhou, Y., Song, R., and Sherwood, B (2018) Quantile-optimal treatment regimes | 1.000 | 6 | 3 | 100% |
| 4 | Kitagawa, T. and Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.978 | 31 | 9 | 94% |
| 5 | Athey, S. and Wager, S (2021) Policy learning with observational data | 0.952 | 29 | 9 | 86% |
| 6 | Luedtke, A. and Chambaz, A (2020) Performance guarantees for policy learning | 0.894 | 7 | 4 | 71% |
| 7 | Athey, S., Imbens, G. W., Metzger, J., and Munro, E (2024) Using wasserstein generative adversarial networks for the design of monte carlo simulations | 0.874 | 6 | 3 | 67% |
| 8 | Rai, Y (2018) Statistical inference for treatment assignment policies | 0.737 | 5 | 3 | 40% |
| 9 | Bloom, H. S., Orr, L. L., Bell, S. H., Cave, G., Doolittle, F., Lin,… (1997) The benefits and costs of jtpa title ii-a programs: Key findings from the national job training partnership act study | 0.737 | 3 | 3 | 67% |
| 10 | Qi, Z., Pang, J.-S., and Liu, Y (2023) On robustness of individualized decision rules | 0.693 | 8 | 1 | 100% |
Showing the top 10 of 74 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Higher-Order Debiased Estimators for General Treatment Models | 0.644 | 2 | 2 |
| 2 | Policy Learning under Unobserved Confounding: A Robust and Efficient Approach | 0.405 | 1 | 1 |