mlsanityData to 14 Oct 2026

Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

arXiv 2105.01187 ↗First posted 3 May 2021stat.ME cs.LG stat.MLcited by 21

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Abstract (arXiv)

Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish several identification results for different classes of ITRs, exhibiting the trade-off between the risk of making untestable assumptions and the value function improvement in decision making. Based on these results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and develop their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and one real data application.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 15 of its 65 references that are in the map. First posted on arXiv 3 May 2021.

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15 of 65 references.

References · 15

  1. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  2. 2020An Introduction to Proximal Causal Learningcited by 95
  3. 2020Minimax Estimation of Conditional Moment Modelscited by 83
  4. 2018Confounding-Robust Policy Improvementcited by 76
  5. 2020Semiparametric proximal causal inferencecited by 59
  6. 2016Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensionscited by 50
  7. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 44
  8. 2015Residual Weighted Learning for Estimating Individualized Treatment Rulescited by 38
  9. 2018A confounding bridge approach for double negative control inference on causal effectscited by 36
  10. 2016Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variablescited by 23
  11. 2019A semiparametric instrumental variable approach to optimal treatment regimes under endogeneitycited by 22
  12. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
3 more
  1. 2019Efficient augmentation and relaxation learning for individualized treatment rules using observational datacited by 9
  2. 2019High dimensional precision medicine from patient-derived xenograftscited by 3
  3. 2018Fast Optimal Bandwidth Selection for RBF Kernel using Reproducing Kernel Hilbert Space Operators for Kernel Based Classifierscited by 1

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Citing papers per year: 2022: 6; 2023: 9; 2024: 0; 2025: 3; 2026: 3.

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  1. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  2. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  3. 2022Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approachcited by 7
  4. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  5. 2023Learning Complementary Policies for Human-AI Teamscited by 7
  6. 2025Unveiling and Causalizing CoT: A Causal Pespectivecited by 6
  7. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  8. 2023Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival datacited by 5
  9. 2023Policy Learning with Distributional Welfarecited by 5
  10. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  11. 2022Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environmentscited by 3
  12. 2022RISE: Robust Individualized Decision Learning with Sensitive Variablescited by 3
9 more
  1. 2023Contextual Dynamic Pricing with Strategic Buyerscited by 3
  2. 2025Reinforcement Learning with Continuous Actions Under Unmeasured Confoundingcited by 3
  3. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  4. 2023Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learningcited by 1
  5. 2026Proximal Path-Specific Inferencecited by 1
  6. 2023A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learningcited by 0
  7. 2025Testing for Causal Fairnesscited by 0
  8. 2026Decision-Aware Proximal Bridge Learning for Optimal Treatment Selectioncited by 0
  9. 2026Proximal Mediation Analysis with Hidden Recanting Witnessescited by 0