Comparer des méthodes

	Analyse de sensibilité pour la causalité en recherche sur l'éducation	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Inférence causale	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	1983–2002	1990s (modern applications)
Auteur d'origine	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal robustness / bias assessment	Method
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: Princeton University Press.
Alias	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	IV, two-stage least squares, TSLS, causal estimation
Apparentées	6	3
Résumé	Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.