قارن الطرق

	خوارزميات اكتشاف السببية (PC، FCI، LiNGAM)	DBSCAN
المجال	الاستدلال السببي	تعلم الآلة
العائلة	Regression model	Machine learning
سنة النشأة	2000	1996
صاحب الطريقة	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
النوع	Causal structure learning	Density-based clustering algorithm
المصدر التأسيسي	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.
الأسماء البديلة	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
ذات صلة	5	3
الملخص	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.