arXiv 8 Apr 2026 · Econometrics
arXiv:2604.07181 · PDF · DOI · OpenAlex · Extracted main text
Empirical research shows that individuals' responses to treatments vary along latent characteristics, such as innate ability or motivation. Therefore, a policymaker seeking to maximize welfare may consider designing policies based on observed characteristics and estimated latent traits. I characterize how the estimates' precision affects the worst-case performance of policies deriving rate-sharp regret bounds for assignment rules that include or exclude them, highlighting new trade-offs with the policy space complexity. I then study how a policymaker can solve such trade-offs by designing tailored data collections and derive a sufficient condition for a collection plan to be minimax optimal. In an empirical application in development economics, I show that including a proxy for entrepreneurs' business skills in targeting cash transfers increases welfare by 5%, and halves the probability of generating welfare losses. Moreover, I estimate the optimal allocation of resources between improving the precision of the proxy via repeated measurements, and increasing sample size.
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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 | Hussam, Reshmaan and Rigol, Natalia and Roth, Benjamin N Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field | 0.969 | 11 | 5 | 91% |
| 2 | Kitagawa, Toru and Tetenov, Aleksey Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.874 | 15 | 5 | 67% |
| 3 | Athey, Susan and Wager, Stefan Policy Learning With Observational Data | 0.737 | 3 | 2 | 100% |
| 4 | Mbakop, Eric and Tabord-Meehan, Max Model Selection for Treatment Choice: Penalized Welfare Maximization | 0.737 | 3 | 2 | 100% |
| 5 | Henderson, J. Vernon and Storeygard, Adam and Weil, David N Measuring Economic Growth from Outer Space | 0.644 | 2 | 2 | 100% |
| 6 | Emily Breza and Arun G. Chandrasekhar and Davide Viviano Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery | 0.511 | 2 | 1 | 100% |
| 7 | Aristotelis Epanomeritakis and Davide Viviano (2025) Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence | 0.511 | 2 | 1 | 100% |
| 8 | Heckman, James J. and Vytlacil, Edward Policy-Relevant Treatment Effects | 0.511 | 2 | 1 | 100% |
| 9 | Heckman, James J. and Vytlacil, Edward Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.511 | 2 | 1 | 100% |
| 10 | Manski, Charles F Statistical Treatment Rules for Heterogeneous Populations | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 40 scored citations.