Comparer des méthodes

	Algorithmes de découverte causale (PC, FCI, LiNGAM)	Identification causale avec les graphes acycliques dirigés (do-calculus)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	2009
Auteur d'origine	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl
Type	Causal structure learning	Causal identification framework
Source fondatrice	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
Alias	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Apparentées	5	5
Résumé	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.	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.