Comparer des méthodes

	Analyse causale de médiation (effets directs et indirects naturels)	Identification causale avec les graphes acycliques dirigés (do-calculus)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	2009
Auteur d'origine	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Judea Pearl
Type	Counterfactual causal decomposition	Causal identification framework
Source fondatrice	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
Alias	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Apparentées	5	5
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.	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.