mlsanityData to 7 Oct 2026

Sensitivity Analysis for Marginal Structural Models

arXiv 2210.04681 ↗First posted 10 Oct 2022stat.ME math.ST stat.THcited by 10

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Abstract (arXiv)

We introduce several methods for assessing sensitivity to unmeasured confounding in marginal structural models; importantly we allow treatments to be discrete or continuous, static or time-varying. We consider three sensitivity models: a propensity-based model, an outcome-based model, and a subset confounding model, in which only a fraction of the population is subject to unmeasured confounding. In each case we develop efficient estimators and confidence intervals for bounds on the causal parameters.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2022.

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Citing papers per year: 2023: 6; 2024: 2; 2025: 2.

10 papers.

  1. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  2. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  3. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  4. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  5. 2023Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounderscited by 12
  6. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  7. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  8. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  9. 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processescited by 3
  10. 2025Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoringcited by 1