Comparer des méthodes

	Analyse de sensibilité pour la causalité en recherche sur l'éducation	Méthodes de jumelage (CEM / Optimal / Génétique)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983–2002	2012
Auteur d'origine	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Iacus, King & Porro (CEM); Hansen (optimal/full matching)
Type	Causal robustness / bias assessment	Matching for causal inference
Source fondatrice	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	coarsened exact matching, optimal matching, genetic matching, CEM
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.	Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching.