mlsanityData to 7 Oct 2026

Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner

arXiv 2411.03387 ↗First posted 5 Nov 2024cs.LG stat.MLcited by 10

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Abstract (arXiv)

Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect. Yet, the aleatoric uncertainty of the treatment effect has received surprisingly little attention in the causal machine learning community. To fill this gap, we aim to quantify the aleatoric uncertainty of the treatment effect at the covariate-conditional level, namely, the conditional distribution of the treatment effect (CDTE). Unlike average causal quantities, the CDTE is not point identifiable without strong additional assumptions. As a remedy, we employ partial identification to obtain sharp bounds on the CDTE and thereby quantify the aleatoric uncertainty of the treatment effect. We then develop a novel, orthogonal learner for the bounds on the CDTE, which we call AU-learner. We further show that our AU-learner has several strengths in that it satisfies Neyman-orthogonality and, thus, quasi-oracle efficiency. Finally, we propose a fully-parametric deep learning instantiation of our AU-learner.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 62 of its 123 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

62 of 123 references.

References · 62

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  3. 2019Neural Spline Flowscited by 590
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  5. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  6. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  7. 2019Orthogonal Statistical Learningcited by 150
  8. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  9. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  10. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 129
  11. 2020An Introduction to Proximal Causal Learningcited by 91
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38 more
  1. 2020Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effectscited by 72
  2. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  3. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  4. 2018Conditional Density Estimation with Bayesian Normalising Flowscited by 54
  5. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  6. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  7. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  8. 2018Counterfactual Mean Embeddingscited by 32
  9. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  10. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  11. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  12. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  13. 2019Noise Regularization for Conditional Density Estimationcited by 23
  14. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  15. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  16. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  17. 2018Visually Communicating and Teaching Intuition for Influence Functionscited by 19
  18. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  19. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  20. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  21. 2017Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithmscited by 16
  22. 2018Causal effects based on distributional distancescited by 15
  23. 2021Autoregressive Quantile Flows for Predictive Uncertainty Estimationcited by 15
  24. 2024Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrastscited by 15
  25. 2022What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatmentcited by 14
  26. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  27. 2020Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantificationcited by 12
  28. 2022Sensitivity analysis under the f-sensitivity models: a distributional robustness perspectivecited by 12
  29. 2020Conformal prediction intervals for the individual treatment effectcited by 10
  30. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  31. 2022Sensitivity Analysis for Marginal Structural Modelscited by 10
  32. 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effectcited by 10
  33. 2024Simplifying debiased inference via automatic differentiation and probabilistic programmingcited by 10
  34. 2022Feature Selection for Discovering Distributional Treatment Effect Modifierscited by 7
  35. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  36. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  37. 2023Adaptive debiased machine learning using data-driven model selection techniquescited by 7
  38. 2023Counterfactual Density Estimation using Kernel Stein Discrepanciescited by 7

50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 6.

10 papers.

  1. 2024Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasetscited by 6
  2. 2025Differentially Private Learners for Heterogeneous Treatment Effectscited by 5
  3. 2025A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatmentscited by 3
  4. 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
  5. 2026Debiased neural operators for estimating functionalscited by 1
  6. 2026Uncertainty-aware Causal Decision Making via Effect Bound Decompositioncited by 0
  7. 2026Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxiescited by 0
  8. 2026Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variationcited by 0
  9. 2026Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Datacited by 0
  10. 2026Path-specific harm decomposition: A partial identification frameworkcited by 0