Comparer des méthodes

	Analyse de sensibilité pour la causalité en recherche sur l'éducation	La régression par discontinuité (RDD)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983–2002	2008
Auteur d'origine	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Type	Causal robustness / bias assessment	Quasi-experimental causal design
Source fondatrice	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Alias	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Apparentées	6	5
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.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.