قارن الطرق

	خوارزميات اكتشاف السببية (PC، FCI، LiNGAM)	انحدار المربعات الصغرى العادية (OLS)
المجال	الاستدلال السببي	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	2000	2019
صاحب الطريقة	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Wooldridge (textbook treatment); classical least squares
النوع	Causal structure learning	Linear regression
المصدر التأسيسي	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
الأسماء البديلة	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).