Compară metode

	Algoritmi de Descoperire Cauzală (PC, FCI, LiNGAM)	Rețeaua de Atenție Grafică
Domeniu	Inferență cauzală	Învățare profundă
Familie	Regression model	Machine learning
Anul apariției	2000	2018
Autorul original	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Veličković, P. et al.
Tip	Causal structure learning	Graph neural network (attention-based)
Sursa seminală	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.
Denumiri alternative	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Înrudite	5	4
Rezumat	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).