ArticleJournal of machine learning research : JMLR2019
Efficient augmentation and relaxation learning for individualized treatment rules using observational data.
Article in Journal of machine learning research : JMLR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed.
- A sparse additive model for treatment effect-modifier selection.Biostatistics (Oxford, England) · 2022Trial
- Article
- Constructing a T-Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data.Statistics in medicine · 2025Article
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.Statistical methods in medical research · 2024Article
- Fusing Individualized Treatment Rules Using Secondary Outcomes.Proceedings of machine learning research · 2024Article
- Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions.The international journal of biostatistics · 2023Article
- Article
- Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome.Statistics in biosciences · 2023Article
- Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes.Journal of machine learning research : JMLR · 2023Article
- BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES.Annals of statistics · 2022Article
- Contrast weighted learning for robust optimal treatment rule estimation.Statistics in medicine · 2022Article
- Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.BMC medical informatics and decision making · 2022Article
- Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.Journal of machine learning research : JMLR · 2022Article
- A constrained single-index regression for estimating interactions between a treatment and covariates.Biometrics · 2021Article
- Discussion of Kallus (2020) and Mo et al (2020).Journal of the American Statistical Association · 2021Article
- Learning Optimal Distributionally Robust Individualized Treatment Rules.Journal of the American Statistical Association · 2021Article
- Improved doubly robust estimation in learning optimal individualized treatment rules.Journal of the American Statistical Association · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access to patient-level data have created an urgent need for high-quality estimators of individualized treatment rules that can be applied to observational data. A recent and promising line of research for estimating individualized treatment rules recasts the problem of estimating an optimal treatment rule as a weighted classification problem. We consider a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers. The proposed class applies to observational data and is doubly-robust in the sense that correct specification of either a propensity or outcome model leads to consistent estimation of the optimal individualized treatment rule. Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods. Simulation experiments informed by these results demonstrate that it is possible to construct new estimators within the proposed framework that significantly outperform existing ones. We illustrate the proposed methods using data from a labor training program and a study of inflammatory bowel syndrome.
Indexed as
Identifiers
31440118PMC6705615What OpenQuestion holds
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.