mlsanityData to 7 Oct 2026

Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap

arXiv 1711.11286 ↗First posted 30 Nov 2017stat.ME math.ST stat.THcited by 33

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Abstract (arXiv)

To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the "missing at random" and "no unmeasured confounder" assumptions. However, these assumptions are unverifiable using empirical data and pose serious threats to the validity of the qualitative conclusions of the statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated to a certain degree. In this paper we consider a marginal sensitivity model which is a natural extension of Rosenbaum's sensitivity model that is widely used for matched observational studies. We aim to construct confidence intervals based on inverse probability weighting estimators, such that asymptotically the intervals have at least nominal coverage of the estimand whenever the data generating distribution is in the collection of marginal sensitivity models. We use a percentile bootstrap and a generalized minimax/maximin inequality to transform this intractable problem to a linear fractional programming problem, which can be solved very efficiently. We illustrate our method using a real dataset to estimate the causal effect of fish consumption on blood mercury level.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Nov 2017.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 11; 2024: 3; 2025: 2; 2026: 6.

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  1. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  2. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  3. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  4. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  5. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  6. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  7. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 12
  8. 2021The s-value: evaluating stability with respect to distributional shiftscited by 10
  9. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  10. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  11. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  12. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
21 more
  1. 2022Robust Design and Evaluation of Predictive Algorithms under Unobserved Confoundingcited by 7
  2. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  3. 2023Confounding-Robust Policy Improvement with Human-AI Teamscited by 4
  4. 2022New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiographycited by 3
  5. 2023A Fast Bootstrap Algorithm for Causal Inference with Large Datacited by 3
  6. 2024Distributionally robust risk evaluation with an isotonic constraintcited by 3
  7. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  8. 2022Distributionally Robust Causal Inference with Observational Datacited by 2
  9. 2023Off-Policy Evaluation with Out-of-Sample Guaranteescited by 2
  10. 2023Kernel Conditional Moment Constraints for Confounding Robust Inferencecited by 2
  11. 2022Towards Robust Off-policy Learning for Runtime Uncertaintycited by 1
  12. 2024Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimizationcited by 1
  13. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1
  14. 2023A Convex Framework for Confounding Robust Inferencecited by 0
  15. 2023Externally Valid Policy Evaluation Combining Trial and Observational Datacited by 0
  16. 2024Valid causal inference with unobserved confounding in high-dimensional settingscited by 0
  17. 2026Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inferencecited by 0
  18. 2026High-Dimensional Gaussian Mean Estimation under Realizable Contaminationcited by 0
  19. 2026Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priorscited by 0
  20. 2026A Practical Upper Bound on Selection Bias Effects in Medical Prediction Modelscited by 0
  21. 2026Conformal Bayes under Continuous Label Shift: Sensitivity Analysis and the Limits of Exact Validitycited by 0