Jarrod Burgh, Emerson Melo
arXiv 24 Sep 2026 · Econometrics
arXiv:2609.30617 · PDF · Extracted main text
This paper studies risk-averse treatment allocation when individuals self-select into treatment based on unobserved characteristics. We develop a framework that combines the marginal treatment effect approach to endogenous selection with a general class of coherent risk measures that capture distributional preferences over welfare outcomes. We show that the planner's problem admits equivalent interpretations in terms of uncertainty aversion, distributional robustness, and worst-case welfare. For law-invariant coherent risk measures, we derive a Kusuoka representation that expresses the planner's objective as a weighted evaluation of different regions of the welfare distribution and characterize the resulting optimal allocation rule. We further establish finite-sample regret guarantees for empirical risk-averse policy learning, showing how the statistical difficulty of learning a policy depends on the planner's sensitivity to adverse welfare outcomes. The framework nests the risk-neutral policy learning model of \cite{Kiatagawa_Tetenov_2018} as a special case. An application to the \cite{Card1995} college proximity data demonstrates that incorporating risk aversion can lead to economically meaningful changes in optimal college admission policies.
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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 | Toru Kitagawa and Aleksey Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 13 | 4 | 100% |
| 2 | Charles F. Manski (2004) Statistical treatment rules for heterogeneous populations | 1.000 | 7 | 3 | 100% |
| 3 | David Card (1995) Using geographic variation in college proximity to estimate the return to schooling | 1.000 | 5 | 4 | 100% |
| 4 | Susan Athey and Stefan Wager (2021) Policy learning with observational data | 0.928 | 4 | 4 | 100% |
| 5 | Yanqin Fan, Yuan Qi, and Gaoqian Xu (2025) Policy learning with $$-expected welfare, 2025 | 0.928 | 4 | 3 | 100% |
| 6 | Yuya Sasaki and Takuya Ura (2024) Welfare analysis via marginal treatment effects | 0.923 | 14 | 5 | 79% |
| 7 | James J. Heckman and Edward Vytlacil (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.843 | 3 | 3 | 100% |
| 8 | Alexander Shapiro (2013) On kusuoka representation of law invariant risk measures | 0.794 | 6 | 4 | 50% |
| 9 | Hans Föllmer and Alexander Schied Stochastic Finance | 0.737 | 5 | 3 | 40% |
| 10 | Shai Shalev-Shwartz and Shai Ben-David (2013) Understanding machine learning: From theory to algorithms | 0.737 | 5 | 3 | 40% |
Showing the top 10 of 43 scored citations.