পদ্ধতির তুলনা করুন

	কার্যকারণ আবিষ্কার অ্যালগরিদম (PC, FCI, LiNGAM)	গ্রাফ অ্যাটেনশন নেটওয়ার্ক
ক্ষেত্র	কার্যকারণ অনুমান	গভীর শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2000	2018
প্রবর্তক	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Veličković, P. et al.
ধরন	Causal structure learning	Graph neural network (attention-based)
মৌলিক উৎস	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.
অপর নাম	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
সম্পর্কিত	5	4
সারসংক্ষেপ	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).