mlsanityData to 7 Oct 2026

Proximal Causal Learning of Conditional Average Treatment Effects

arXiv 2301.10913 ↗First posted 26 Jan 2023stat.ML cs.LG stat.MEcited by 7

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Abstract (arXiv)

Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption that the measured covariates are enough to justify conditional exchangeability. We propose the P-learner, motivated by the R- and DR-learner, a tailored two-stage loss function for learning heterogeneous treatment effects in settings where exchangeability given observed covariates is an implausible assumption, and we wish to rely on proxy variables for causal inference. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods, which in the case of kernel regression satisfies an oracle bound on the estimated error as long as the nuisance components are estimated reasonably well.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 37 of its 74 references that are in the map. First posted on arXiv 26 Jan 2023.

Builds on

37 of 74 references.

References · 37

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
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  9. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
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  11. 2019Orthogonal Statistical Learningcited by 150
  12. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
25 more
  1. 2020An Introduction to Proximal Causal Learningcited by 91
  2. 2020Minimax Estimation of Conditional Moment Modelscited by 83
  3. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  4. 2020Semiparametric proximal causal inferencecited by 58
  5. 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 54
  6. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  7. 2021Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processescited by 40
  8. 2020Adversarial Estimation of Riesz Representerscited by 34
  9. 2019Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulationscited by 32
  10. 2019Machine Learning Estimation of Heterogeneous Treatment Effects with Instrumentscited by 29
  11. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
  12. 2020Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instrumentscited by 27
  13. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  14. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  15. 2021Proximal Causal Inference for Complex Longitudinal Studiescited by 16
  16. 2022Deep Learning Methods for Proximal Inference via Maximum Moment Restrictioncited by 13
  17. 2021The Proximal ID Algorithmcited by 11
  18. 2021Proximal mediation analysiscited by 10
  19. 2021Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Frameworkcited by 9
  20. 2020An Adversarial Approach to Structural Estimationcited by 8
  21. 2022Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectivenesscited by 5
  22. 2022Proximal Causal Inference for Marginal Counterfactual Survival Curvescited by 5
  23. 2022Proximal Survival Analysis to Handle Dependent Right Censoringcited by 5
  24. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  25. 2021Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Modelscited by 4

Built on by

Cited by year (2026 to 7 Oct)

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

7 papers.

  1. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  2. 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
  3. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
  4. 2025Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimandscited by 5
  5. 2024Recovering Latent Confounders from High-dimensional Proxy Variablescited by 0
  6. 2026Proximal Mediation Analysis with Hidden Recanting Witnessescited by 0
  7. 2026What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Viewscited by 0