mlsanityData to 7 Oct 2026

Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing

arXiv 2102.04543 ↗First posted 8 Feb 2021math.ST econ.EM stat.MEcited by 32

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Abstract (arXiv)

Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This paper introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assumptions) that must contain the true treatment effect. Our proposal is a refinement of the influential sensitivity analysis by Zhao, Small, and Bhattacharya (2019), which we show gives bounds that are too wide even asymptotically. This analysis is based on new partial identification results for Tan (2006)'s marginal sensitivity model.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2021.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 13; 2024: 5; 2025: 4; 2026: 5.

32 papers.

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  1. 2024Causal machine learning for predicting treatment outcomescited by 73
  2. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  3. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  4. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  5. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  6. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  7. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  8. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  9. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  10. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  11. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 12
  12. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
20 more
  1. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  2. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  3. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  4. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  5. 2022Robust Design and Evaluation of Predictive Algorithms under Unobserved Confoundingcited by 7
  6. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  7. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  8. 2023Confounding-Robust Policy Improvement with Human-AI Teamscited by 4
  9. 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processescited by 3
  10. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  11. 2023Kernel Conditional Moment Constraints for Confounding Robust Inferencecited by 2
  12. 2024Defining Expertise: Applications to Treatment Effect Estimationcited by 2
  13. 2026Information-Theoretic Causal Bounds under Unmeasured Confoundingcited by 2
  14. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  15. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  16. 2023A Convex Framework for Confounding Robust Inferencecited by 0
  17. 2023Externally Valid Policy Evaluation Combining Trial and Observational Datacited by 0
  18. 2025Data Fusion for High-Resolution Estimationcited by 0
  19. 2026TEA-Time: Transporting Effects Across Timecited by 0
  20. 2026Learning Risk Scores Robust to Unobserved Confounderscited by 0