mlsanityData to 7 Oct 2026

Differentially Private Learners for Heterogeneous Treatment Effects

arXiv 2503.03486 ↗First posted 5 Mar 2025cs.LG cs.CRcited by 5

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Abstract (arXiv)

Patient data is widely used to estimate heterogeneous treatment effects and thus understand the effectiveness and safety of drugs. Yet, patient data includes highly sensitive information that must be kept private. In this work, we aim to estimate the conditional average treatment effect (CATE) from observational data under differential privacy. Specifically, we present DP-CATE, a novel framework for CATE estimation that is Neyman-orthogonal and further ensures differential privacy of the estimates. Our framework is highly general: it applies to any two-stage CATE meta-learner with a Neyman-orthogonal loss function, and any machine learning model can be used for nuisance estimation. We further provide an extension of our DP-CATE, where we employ RKHS regression to release the complete CATE function while ensuring differential privacy. We demonstrate our DP-CATE across various experiments using synthetic and real-world datasets. To the best of our knowledge, we are the first to provide a framework for CATE estimation that is Neyman-orthogonal and differentially private.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 68 references that are in the map. First posted on arXiv 5 Mar 2025.

Builds on

35 of 68 references.

References · 35

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  3. 2019Orthogonal Statistical Learningcited by 150
  4. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  5. 2020Understanding Gradient Clipping in Private SGD: A Geometric Perspectivecited by 140
  6. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 129
  7. 2024Causal machine learning for predicting treatment outcomescited by 73
  8. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  9. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  10. 2018Orthogonal Random Forest for Causal Inferencecited by 51
  11. 2020Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curvescited by 36
  12. 2017Orthogonal Machine Learning: Power and Limitationscited by 28
23 more
  1. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  2. 2022Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimizationcited by 22
  3. 2017Differentially Private Empirical Risk Minimization with Input Perturbationcited by 19
  4. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  5. 2021Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacycited by 18
  6. 2024Model-agnostic meta-learners for estimating heterogeneous treatment effects over timecited by 18
  7. 2019Privacy-preserving parametric inference: a case for robust statisticscited by 17
  8. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  9. 2018Differentially Private Confidence Intervals for Empirical Risk Minimizationcited by 11
  10. 2023Fair Off-Policy Learning from Observational Datacited by 11
  11. 2023Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learnerscited by 11
  12. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  13. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  14. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  15. 2019Privacy-Preserving Causal Inference via Inverse Probability Weightingcited by 9
  16. 2023On Weighted Orthogonal Learners for Heterogeneous Treatment Effectscited by 8
  17. 2023Locally Private Causal Inference for Randomized Experimentscited by 7
  18. 2024Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomescited by 6
  19. 2023Consistent End-to-End Estimation for Counterfactual Fairnesscited by 4
  20. 2024Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trialscited by 4
  21. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  22. 2023Improving the Variance of Differentially Private Randomized Experiments through Clusteringcited by 2
  23. 2022Differentially Private Stochastic Gradient Descent with Low-Noisecited by 1

Built on by

Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2025Model Agnostic Differentially Private Causal Inferencecited by 2
  2. 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
  3. 2026ORTHOBO: Orthogonal Bayesian Hyperparameter Optimizationcited by 0
  4. 2026Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibrationcited by 0
  5. 2026Differentially Private Average Treatment Effect Estimation by Propensity Score Blockingcited by 0