mlsanityData to 7 Oct 2026

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

arXiv 1810.02894 ↗First posted 5 Oct 2018stat.ML cs.LGcited by 51

Not on the map. Why?

Abstract (arXiv)

We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedness, i.e., when there are no unobserved confounders. Since CATE may not be identified when unconfoundedness is violated, we develop a functional interval estimator that predicts bounds on the individual causal effects under realistic violations of unconfoundedness. Our estimator takes the form of a weighted kernel estimator with weights that vary adversarially. We prove that our estimator is sharp in that it converges exactly to the tightest bounds possible on CATE when there may be unobserved confounders. Further, we study personalized decision rules derived from our estimator and prove that they achieve optimal minimax regret asymptotically. We assess our approach in a simulation study as well as demonstrate its application in the case of hormone replacement therapy by comparing conclusions from a real observational study and clinical trial.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 7; 2023: 12; 2024: 5; 2025: 11; 2026: 3.

51 papers.

Sort:

By area:

  1. 2024Causal machine learning for predicting treatment outcomescited by 73
  2. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  3. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  4. 2021Instrumental Variable Value Iteration for Causal Offline Reinforcement Learningcited by 35
  5. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  6. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  7. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  8. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 27
  9. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  10. 2021Long Story Short: Omitted Variable Bias in Causal Machine Learningcited by 23
  11. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  12. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
38 more
  1. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 17
  2. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  3. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  4. 2022What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatmentcited by 14
  5. 2021Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variablecited by 13
  6. 2022Model-Free and Model-Based Policy Evaluation when Causality is Uncertaincited by 12
  7. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 12
  8. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  9. 2021Intact-VAE: Estimating Treatment Effects under Unobserved Confoundingcited by 10
  10. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  11. 2024Conformal Counterfactual Inference under Hidden Confoundingcited by 10
  12. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  13. 2021Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradoxcited by 8
  14. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  15. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  16. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  17. 2022Robust Design and Evaluation of Predictive Algorithms under Unobserved Confoundingcited by 7
  18. 2023Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confoundingcited by 7
  19. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  20. 2024Stabilized Neural Prediction of Potential Outcomes in Continuous Timecited by 7
  21. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  22. 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
  23. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  24. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  25. 2021SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inferencecited by 3
  26. 2025Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutionscited by 3
  27. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  28. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  29. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  30. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  31. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  32. 2021Adaptive Multi-Source Causal Inferencecited by 0
  33. 2021H"older Bounds for Sensitivity Analysis in Causal Reasoningcited by 0
  34. 2023Mind the Gap: Optimal and Equitable Encouragement Policiescited by 0
  35. 2025Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspectivecited by 0
  36. 2025Conditional Average Treatment Effect Estimation Under Hidden Confounderscited by 0
  37. 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridgecited by 0
  38. 2025Assessment of the conditional exchangeability assumption in causal machine learning models: a simulation studycited by 0

50 most cited of 51 All 51 (JSON)