Comparer des méthodes

	Algorithmes de découverte causale (PC, FCI, LiNGAM)	Réseau d'attention sur graphe
Domaine	Inférence causale	Apprentissage profond
Famille	Regression model	Machine learning
Année d'origine	2000	2018
Auteur d'origine	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Veličković, P. et al.
Type	Causal structure learning	Graph neural network (attention-based)
Source fondatrice	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Alias	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Apparentées	5	4
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.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).