Comparer des méthodes

	Analyse causale de médiation (effets directs et indirects naturels)	Modélisation Linéaire Hiérarchique (HLM / Modélisation Multiniveaux)
Domaine	Inférence causale	Statistique
Famille	Regression model	Hypothesis test
Année d'origine	2010	1986
Auteur d'origine	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Type	Counterfactual causal decomposition	Parametric nested-data regression
Source fondatrice	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Alias	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	HLM, MLM, multilevel modeling, multilevel analysis
Apparentées	5	4
Résumé	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.