mlsanityData to 14 Oct 2026

Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding

arXiv 2112.11449 ↗First posted 21 Dec 2021stat.ME cs.LG econ.EMcited by 28

Not on the map. Why?

Abstract (arXiv)

We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders could not change the odds of treatment for any unit by more than a fixed factor. We derive the sharp partial identification bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The first is double sharpness: our estimators consistently estimate the sharp ATE bounds when one of two nuisance parameters is misspecified and achieve semiparametric efficiency when all nuisance parameters are suitably consistent. The second is double validity: even when most nuisance parameters are misspecified, our estimators still provide valid but possibly conservative bounds for the ATE and our Wald confidence intervals remain valid even when our estimators are not asymptotically normal. As a result, our estimators provide a highly credible method for sensitivity analysis of causal inferences.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 23 of its 91 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

23 of 91 references.

References · 23

  1. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  2. 2016Generalized Random Forestscited by 312
  3. 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 291
  4. 2019Orthogonal Statistical Learningcited by 151
  5. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  6. 2018Confounding-Robust Policy Improvementcited by 76
  7. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  8. 2020Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learningcited by 57
  9. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  10. 2021Conformal Sensitivity Analysis for Individual Treatment Effectscited by 40
  11. 2018Bounds on the conditional and average treatment effect with unobserved confounding factorscited by 39
  12. 2017Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrapcited by 33
11 more
  1. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  2. 2017Orthogonal Machine Learning: Power and Limitationscited by 28
  3. 2020Combining Observational and Experimental Datasets Using Shrinkage Estimatorscited by 28
  4. 2014Robust Sample Average Approximationcited by 25
  5. 2019Sensitivity analysis via the proportion of unmeasured confoundingcited by 13
  6. 2019Selective machine learning of doubly robust functionalscited by 9
  7. 2015On the Dual Representation of Coherent Risk Measurescited by 8
  8. 2021Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locationscited by 8
  9. 2021Interpretable Sensitivity Analysis for Balancing Weightscited by 5
  10. 2021Designing Experiments Informed by Observational Studiescited by 3
  11. 2020Doubly Robust Semiparametric Inference Using Regularized Calibrated Estimation with High-dimensional Datacited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 8; 2023: 9; 2024: 3; 2025: 4; 2026: 4.

28 papers.

Sort:

By area:

  1. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  2. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  3. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  4. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  5. 2022What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatmentcited by 15
  6. 2022Learning from a Biased Samplecited by 14
  7. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 13
  8. 2022Treatment Effect Risk: Bounds and Inferencecited by 12
  9. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  10. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  11. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  12. 2022Robust Design and Evaluation of Predictive Algorithms under Unobserved Confoundingcited by 8
16 more
  1. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  2. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  3. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 6
  4. 2024Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Datacited by 5
  5. 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processescited by 3
  6. 2025Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inferencecited by 3
  7. 2022Distributionally Robust Causal Inference with Observational Datacited by 2
  8. 2023Kernel Conditional Moment Constraints for Confounding Robust Inferencecited by 2
  9. 2026Causal methods for LLM development and evaluationcited by 2
  10. 2022Data-Driven Influence Functions for Optimization-Based Causal Inferencecited by 1
  11. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  12. 2026Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Frameworkcited by 1
  13. 2023A Convex Framework for Confounding Robust Inferencecited by 0
  14. 2025A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confoundingcited by 0
  15. 2026ConfoundingSHAP: Quantifying confounding strength in causal inferencecited by 0
  16. 2026Learning Risk Scores Robust to Unobserved Confounderscited by 0