ArticleBiometrika2025
Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies.
Article in Biometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Enhanced Marginal Sensitivity Model and Bounds.Journal of the American Statistical Association · 2026Article
- Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.Observational studies · 2026Article
- Letter to the Editor in response to the article 'Retrospective analysis of influences of sacubitril/valsartan in patients with diabetes with poorly controlled blood pressure in Japan'.Journal of diabetes investigation · 2025Article
- Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife.Statistical methods in medical research · 2025Article
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Abstract
We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only counterfactual outcomes, whereas the joint and product sensitivity models involve both counterfactual covariates and outcomes. We establish and compare explicit representations for the sharp and conservative bounds at the population level through convex optimization, depending only on the observed data. These results provide for the first time a satisfactory generalization from the marginal sensitivity model in the cross-sectional setting.
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