arXiv 10 Oct 2022 · Econometrics
arXiv:2210.04703 · PDF · DOI · OpenAlex · Extracted main text
I study the problem of a decision maker choosing a policy which allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear- and integer-programming formulation. I prove the maximum regret of the estimated policy converges to the lowest possible maximum regret at a rate which is the maximum of N^-1/2 and the rate at which conditional average treatment effects are estimated in the experimental data. In an application to designing targeted subsidies for electrical grid connections in rural Kenya, I find that nearly the entire population should be given a treatment not implemented in the experiment, reducing maximum regret by over 60% compared to the policy that restricts to the treatments implemented in the experiment.
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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, Toru, Tetenov, Aleksey (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.971 | 12 | 6 | 92% |
| 2 | Manski, Charles F (2004) Statistical Treatment Rules for Heterogeneous Populations | 0.928 | 4 | 3 | 100% |
| 3 | Mbakop, Eric, Tabord-Meehan, Max (2021) Model Selection for Treatment Choice: Penalized Welfare Maximization | 0.843 | 3 | 3 | 100% |
| 4 | Athey, Susan, Wager, Stefan (2021) Policy Learning With Observational Data | 0.811 | 4 | 2 | 100% |
| 5 | Manski, Charles F (2007) Minimax-Regret Treatment Choice with Missing Outcome Data | 0.811 | 4 | 2 | 100% |
| 6 | D’Adamo, Riccardo (2023) Orthogonal Policy Learning Under Ambiguity | 0.737 | 3 | 2 | 100% |
| 7 | Lee, Kenneth, Miguel, Edward, Wolfram, Catherine (2020) Experimental Evidence on the Economics of Rural Electrification | 0.737 | 3 | 2 | 100% |
| 8 | Manski, Charles F (2006) Search Profiling with Partial Knowledge of Deterrence | 0.737 | 3 | 2 | 100% |
| 9 | Manski, Charles F (2025) Using Limited Trial Evidence to Credibly Choose Treatment Dosage When Efficacy and Adverse Effects Weakly Increase with Dose | 0.693 | 5 | 1 | 100% |
| 10 | Manski, Charles F (1997) Monotone Treatment Response | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning under Biased Sample Selection | 0.511 | 2 | 1 |
| 2 | A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances | 0.405 | 1 | 1 |
| 3 | Nonparametric Bayesian Policy Learning | 0.405 | 1 | 1 |