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| Analyse de sensibilité au biais caché (Bornes de Rosenbaum / E-value)× | Appariement par score de propension× | |
|---|---|---|
| Domaine≠ | Inférence causale | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2002 | 1983 |
| Auteur d'origine≠ | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) | Paul Rosenbaum and Donald Rubin |
| Type≠ | Sensitivity analysis for causal inference | Method |
| Source fondatrice≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Alias≠ | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity | PSM, propensity score weighting, covariate balance |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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