# Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data arXiv: https://arxiv.org/abs/2301.05491 ยท HTML: https://mlsanity.com/paper/2301.05491/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 111 references that are in the map. - First posted on arXiv 13 Jan 2023. ## Builds on (most cited first, 30 of 35) - [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017) - [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016) - [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022) - [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020) - [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021) - [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022) - [Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality](https://mlsanity.com/paper/2212.09900/) (2022) - [Program Evaluation and Causal Inference with High-Dimensional Data](https://mlsanity.com/paper/1311.2645/) (2013) - [Estimating heterogeneous treatment effects with right-censored data via causal survival forests](https://mlsanity.com/paper/2001.09887/) (2020) - [Learning Optimal Distributionally Robust Individualized Treatment Rules](https://mlsanity.com/paper/2006.15121/) (2020) - [Semi-Parametric Efficient Policy Learning with Continuous Actions](https://mlsanity.com/paper/1905.10116/) (2019) - [Fast Rates for the Regret of Offline Reinforcement Learning](https://mlsanity.com/paper/2102.00479/) (2021) - [Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals](https://mlsanity.com/paper/1709.04589/) (2017) - [More Efficient Policy Learning via Optimal Retargeting](https://mlsanity.com/paper/1906.08611/) (2019) - [Sharp instruments for classifying compliers and generalizing causal effects](https://mlsanity.com/paper/1801.03635/) (2018) - [A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity](https://mlsanity.com/paper/1911.09260/) (2019) - [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022) - [Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding](https://mlsanity.com/paper/2105.01187/) (2021) - [Interpretable Dynamic Treatment Regimes](https://mlsanity.com/paper/1606.01472/) (2016) - [Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation](https://mlsanity.com/paper/2005.10579/) (2020) - [Learning from a Biased Sample](https://mlsanity.com/paper/2209.01754/) (2022) - [Multi-Source Causal Inference Using Control Variates](https://mlsanity.com/paper/2103.16689/) (2021) - [Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes](https://mlsanity.com/paper/1504.07715/) (2015) - [Falsification before Extrapolation in Causal Effect Estimation](https://mlsanity.com/paper/2209.13708/) (2022) - [On Estimation of Optimal Treatment Regimes For Maximizing t-Year Survival Probability](https://mlsanity.com/paper/1407.7820/) (2014) - [Improving trial generalizability using observational studies](https://mlsanity.com/paper/2003.01242/) (2020) - [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022) - [Tree based weighted learning for estimating individualized treatment rules with censored data](https://mlsanity.com/paper/1707.09632/) (2017) - [Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring](https://mlsanity.com/paper/2012.03294/) (2020) - [Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes](https://mlsanity.com/paper/1702.04682/) (2017) ## Built on by (most cited first, 5 of 5) - [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023) - [Learning Robust Treatment Rules for Censored Data](https://mlsanity.com/paper/2408.09155/) (2024) - [A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning](https://mlsanity.com/paper/2310.09545/) (2023) - [Learning Survival Models with Right-Censored Reporting Delays](https://mlsanity.com/paper/2510.04421/) (2025) - [Efficient Neural Network Model Selection for Few-Class Application Datasets](https://mlsanity.com/paper/2606.19712/) (2026) ## Abstract (arXiv) An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a counterfactual world had this ITR been implemented. Recently, there has been increasing interest in combining heterogeneous data sources, such as leveraging the complementary features of randomized controlled trial (RCT) data and a large observational study (OS). Usually, a covariate shift exists between the source and target population, rendering the source-optimal ITR unnecessarily optimal for the target population. We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population. The value function accommodates a broad class of functionals of survival distributions, including survival probabilities and restrictive mean survival times (RMSTs). We propose a doubly robust estimator of the value function, and the optimal ITR is learned by maximizing the value function within a pre-specified class of ITRs. We establish the N-1/3 rate of convergence for the estimated parameter indexing the optimal ITR, and show that the proposed optimal value estimator is consistent and asymptotically normal even with flexible machine learning methods for nuisance parameter estimation. We evaluate the empirical performance of the proposed method by simulation studies and a real data application of sodium bicarbonate therapy for patients with severe metabolic acidaemia in the intensive care unit (ICU), combining a RCT and an observational study with heterogeneity.