Comparer des méthodes

	Analyse de sensibilité au biais caché (Bornes de Rosenbaum / E-value)	Ajustement par la porte de devant (Critère de la porte de devant)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2002	1995
Auteur d'origine	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)	Judea Pearl
Type	Sensitivity analysis for causal inference	Causal identification (graphical adjustment)
Source fondatrice	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.
Alias	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
Apparentées	5	4
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).	Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.