mlsanityData to 7 Oct 2026

Optimal Treatment Regimes for Proximal Causal Learning

arXiv 2212.09494 ↗First posted 19 Dec 2022stat.ME stat.MLcited by 5

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Abstract (arXiv)

A common concern when a policymaker draws causal inferences from and makes decisions based on observational data is that the measured covariates are insufficiently rich to account for all sources of confounding, i.e., the standard no confoundedness assumption fails to hold. The recently proposed proximal causal inference framework shows that proxy variables that abound in real-life scenarios can be leveraged to identify causal effects and therefore facilitate decision-making. Building upon this line of work, we propose a novel optimal individualized treatment regime based on so-called outcome and treatment confounding bridges. We then show that the value function of this new optimal treatment regime is superior to that of existing ones in the literature. Theoretical guarantees, including identification, superiority, excess value bound, and consistency of the estimated regime, are established. Furthermore, we demonstrate the proposed optimal regime via numerical experiments and a real data application.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 76 references that are in the map. First posted on arXiv 19 Dec 2022.

Builds on

35 of 76 references.

References · 35

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23 more
  1. 2021Instrumental Variable Value Iteration for Causal Offline Reinforcement Learningcited by 35
  2. 2020Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instrumentscited by 27
  3. 2019A semiparametric instrumental variable approach to optimal treatment regimes under endogeneitycited by 22
  4. 2019Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inferencecited by 21
  5. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  6. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
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  18. 2021Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradoxcited by 8
  19. 2022Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectivenesscited by 5
  20. 2022Proximal Causal Inference for Marginal Counterfactual Survival Curvescited by 5
  21. 2020Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatmentscited by 3
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 3.

5 papers.

  1. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  2. 2023Policy Learning with Distributional Welfarecited by 5
  3. 2026Proximal Path-Specific Inferencecited by 1
  4. 2026Decision-Aware Proximal Bridge Learning for Optimal Treatment Selectioncited by 0
  5. 2026Proximal Mediation Analysis with Hidden Recanting Witnessescited by 0