mlsanityData to 7 Oct 2026

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

arXiv 2103.04850 ↗First posted 8 Mar 2021cs.LG stat.MLcited by 50

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Abstract (arXiv)

We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance -- a level of unidentifiability -- about an individual's response to treatment by inducing bias in CATE estimates. We present a new parametric interval estimator suited for high-dimensional data, that estimates a range of possible CATE values when given a predefined bound on the level of hidden confounding. Further, previous interval estimators do not account for ignorance about the CATE associated with samples that may be underrepresented in the original study, or samples that violate the overlap assumption. Our interval estimator also incorporates model uncertainty so that practitioners can be made aware of out-of-distribution data. We prove that our estimator converges to tight bounds on CATE when there may be unobserved confounding, and assess it using semi-synthetic, high-dimensional datasets.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

13 of 46 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  7. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  8. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  9. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  10. 2018Bounds on the conditional and average treatment effect with unobserved confounding factorscited by 39
  11. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
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1 more
  1. 2020Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effectscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 12; 2023: 12; 2024: 5; 2025: 10; 2026: 4.

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  1. 2021Causal Effect Inference for Structured Treatmentscited by 38
  2. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  3. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 27
  4. 2023Bayesian Neural Controlled Differential Equations for Treatment Effect Estimationcited by 26
  5. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  6. 2021Long Story Short: Omitted Variable Bias in Causal Machine Learningcited by 23
  7. 2022Predicting the impact of treatments over time with uncertainty aware neural differential equationscited by 23
  8. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  9. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  10. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  11. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  12. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 17
38 more
  1. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
  2. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  3. 2021Causal Multi-Agent Reinforcement Learning: Review and Open Problemscited by 16
  4. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  5. 2022Towards assessing agricultural land suitability with causal machine learningcited by 11
  6. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  7. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  8. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  9. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  10. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  11. 2023Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confoundingcited by 7
  12. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  13. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  14. 2021Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacificcited by 6
  15. 2022Quantitative probing: Validating causal models using quantitative domain knowledgecited by 6
  16. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  17. 2022Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Makingcited by 4
  18. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  19. 2022Causal Inference from Small High-dimensional Datasetscited by 3
  20. 2023To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Datacited by 3
  21. 2024Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimationcited by 3
  22. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
  23. 2025The Limits of Predicting Agents from Behaviourcited by 3
  24. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  25. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  26. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  27. 2025Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimationcited by 1
  28. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  29. 2021H"older Bounds for Sensitivity Analysis in Causal Reasoningcited by 0
  30. 2022Prescriptive maintenance with causal machine learningcited by 0
  31. 2022Big Earth Data and Machine Learning for Sustainable and Resilient Agriculturecited by 0
  32. 2022Using uncertainty-aware machine learning models to study aerosol-cloud interactionscited by 0
  33. 2024Causal Effect Estimation Using Random Hyperplane Tessellationscited by 0
  34. 2025Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspectivecited by 0
  35. 2025Mitigating Hidden Confounding by Progressive Confounder Imputation via Large Language Modelscited by 0
  36. 2026Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomescited by 0
  37. 2026Treatment Effect Estimation with Differentiated Networked Effect on Graph Datacited by 0
  38. 2026What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Viewscited by 0