ArticleAnnales de l'I.H.P. Probabilites et statistiques2020
Performance Guarantees for Policy Learning.
Article in Annales de l'I.H.P. Probabilites et statistiques, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 10 citations in OpenAlex.
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Rejoinder: Optimal individualized decision rules using instrumental variable methods.Journal of the American Statistical Association · 2021Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
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Abstract
This article gives performance guarantees for the regret decay in optimal policy estimation. We give a margin-free result showing that the regret decay for estimating a within-class optimal policy is second-order for empirical risk minimizers over Donsker classes when the data are generated from a fixed data distribution that does not change with sample size, with regret decaying at a faster rate than the standard error of an efficient estimator of the value of an optimal policy. We also present a result giving guarantees on the regret decay of policy estimators for the case that the policy falls within a restricted class and the data are generated from local perturbations of a fixed distribution, where this guarantee is uniform in the direction of the local perturbation. Finally, we give a result from the classification literature that shows that faster regret decay is possible via plug-in estimation provided a margin condition holds. Three examples are considered. In these examples, the regret is expressed in terms of either the mean value or the median value, and the number of possible actions is either two or finitely many.
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