পদ্ধতির তুলনা করুন

	কার্যকারণ আবিষ্কার অ্যালগরিদম (PC, FCI, LiNGAM)	ডিবিস্ক্যান
ক্ষেত্র	কার্যকারণ অনুমান	যন্ত্র শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2000	1996
প্রবর্তক	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
ধরন	Causal structure learning	Density-based clustering algorithm
মৌলিক উৎস	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.
অপর নাম	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
সম্পর্কিত	5	3
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.	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.