ArticleJournal of the American Statistical Association2021
Learning Optimal Distributionally Robust Individualized Treatment Rules.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Estimating individualized treatment effects using an individual participant data meta-analysis.BMC medical research methodology · 2024Pooled it
- Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity.Statistics in medicine · 2026Article
- Robust transfer learning for individualized treatment rules in the presence of missing data.Biostatistics (Oxford, England) · 2025Article
- FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.The annals of applied statistics · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Optimizing personalized treatments for targeted patient populations across multiple domains.The international journal of biostatistics · 2024Article
- Efficient Estimation under Data Fusion.Biometrika · 2023Article
- Transfer learning of individualized treatment rules from experimental to real-world data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules.Journal of the American Statistical Association · 2021Article
- Discussion of and : New Objectives for Policy Learning.Journal of the American Statistical Association · 2021Article
- Discussion of Kallus (2020) and Mo et al (2020).Journal of the American Statistical Association · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the training and testing distributions are not identical. In this paper, we consider the problem of finding an optimal ITR from a restricted ITR class where there is some unknown covariate changes between the training and testing distributions. We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are "close" to the training distribution. The resulting DR-ITR can guarantee the performance among all such distributions reasonably well. We further propose a calibrating procedure that tunes the DR-ITR adaptively to a small amount of calibration data from a target population. In this way, the calibrated DR-ITR can be shown to enjoy better generalizability than the standard ITR based on our numerical studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.