Robust Q-Learning
From MaRDI portal
Publication:5857152
DOI10.1080/01621459.2020.1753522zbMath1457.62341arXiv2003.12427OpenAlexW3016676601MaRDI QIDQ5857152
Ashkan Ertefaie, James R. McKay, Robert L. Strawderman, David W. Oslin
Publication date: 30 March 2021
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.12427
Asymptotic properties of parametric estimators (62F12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Analysis of variance and covariance (ANOVA) (62J10)
Related Items
Rejoinder to “Reader reaction to ‘Outcome‐adaptive Lasso: Variable selection for causal inference’ by Shortreed and Ertefaie (2017)”, Optimal Treatment Regimes: A Review and Empirical Comparison, Flexible inference of optimal individualized treatment strategy in covariate adjusted randomization with multiple covariates, Generalization error bounds of dynamic treatment regimes in penalized regression-based learning
Uses Software
Cites Work
- Unnamed Item
- \(Q\)- and \(A\)-learning methods for estimating optimal dynamic treatment regimes
- Valid post-selection inference
- Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine
- Dynamic treatment regimes: technical challenges and applications
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Asymptotics of cross-validated risk estimation in estimator selection and performance assess\-ment
- High-dimensional \(A\)-learning for optimal dynamic treatment regimes
- \({\mathcal Q}\)-learning
- Unified methods for censored longitudinal data and causality
- Semiparametric theory and missing data.
- Consistency of random forests
- Doubly-robust dynamic treatment regimen estimation via weighted least squares
- Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m ‐Out‐of‐ n Bootstrap Scheme
- Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer
- Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules
- Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research
- Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
- Oracle inequalities for multi-fold cross validation
- Root-N-Consistent Semiparametric Regression
- Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Optimal Dynamic Treatment Regimes
- Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
- A Robust Method for Estimating Optimal Treatment Regimes
- Double/debiased machine learning for treatment and structural parameters
- New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
- Bias-Reduced Doubly Robust Estimation
- Doubly robust nonparametric inference on the average treatment effect
- Doubly‐Robust Estimators of Treatment‐Specific Survival Distributions in Observational Studies with Stratified Sampling
- Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
- Super Learner
