mlsanityData to 7 Oct 2026

Conformal Sensitivity Analysis for Individual Treatment Effects

arXiv 2112.03493 ↗First posted 7 Dec 2021stat.MEcited by 40

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Abstract (arXiv)

Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this paper proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unmeasured confounding through a marginal sensitivity model [Ros2002, Tan2006], and adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis problem as a conformal inference problem under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The result is a predictive interval that has guaranteed nominal coverage of the ITE, a method that provides coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 10; 2024: 8; 2025: 5; 2026: 10.

40 papers.

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  1. 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networkscited by 79
  2. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  3. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  4. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  5. 2022Conformal Off-Policy Prediction in Contextual Banditscited by 25
  6. 2023Adaptive conformal classification with noisy labelscited by 24
  7. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  8. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  9. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  10. 2022Conformal Off-policy Predictioncited by 14
  11. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  12. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
28 more
  1. 2025Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbationscited by 11
  2. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  3. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  4. 2024Conformal Counterfactual Inference under Hidden Confoundingcited by 10
  5. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  6. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  7. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  8. 2023Max-Rank: Efficient Multiple Testing for Conformal Predictioncited by 6
  9. 2024What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systemscited by 5
  10. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  11. 2022Distribution-Free Finite-Sample Guarantees and Split Conformal Predictioncited by 4
  12. 2023Confounding-Robust Policy Improvement with Human-AI Teamscited by 4
  13. 2024Uncertainty quantification in metric spacescited by 3
  14. 2024Conditional Prediction ROC Bands for Graph Classificationcited by 3
  15. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  16. 2023Causal Dependence Plotscited by 2
  17. 2025Residual Reweighted Conformal Prediction for Graph Neural Networkscited by 2
  18. 2025Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spacescited by 2
  19. 2025Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inferencecited by 2
  20. 2026Multi-Distribution Robust Conformal Predictioncited by 1
  21. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  22. 2026Conformal Risk-Averse Decision Making with Action Conditional Guaranteecited by 1
  23. 2026Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networkscited by 1
  24. 2026Elements of Conformal Predictioncited by 0
  25. 2026Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Predictioncited by 0
  26. 2026The Exceedance Design Effect: Effective Sample Size for Thresholds under Clusteringcited by 0
  27. 2026Conformal Individual Treatment Effect Estimation under Networked Interferencecited by 0
  28. 2026Conformal Bayes under Continuous Label Shift: Sensitivity Analysis and the Limits of Exact Validitycited by 0