Comparer des méthodes

	Analyse de sensibilité au biais caché (Bornes de Rosenbaum / E-value)	Variables instrumentales par moindres carrés en deux étapes (VI/2SLS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2002	2009
Auteur d'origine	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Sensitivity analysis for causal inference	Instrumental-variables regression
Source fondatrice	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Apparentées	5	5
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).	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).