Comparer des méthodes

	Analyse des processus conditionnels (médiation modérée)	Analyse causale de médiation (effets directs et indirects naturels)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2018	2010
Auteur d'origine	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Pearl (2001); general framework by Imai, Keele & Tingley (2010)
Type	Regression-based conditional process model	Counterfactual causal decomposition
Source fondatrice	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.
Alias	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
Apparentées	5	5
Résumé	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	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.