Krahasoni metodat

	Algoritme zbulimi kauzal (PC, FCI, LiNGAM)	Identifikimi kauzal me Grafet Drejt-ciklike (do-calculus)	Regresioni me Mënyrën më të Vogël të Katrorëve (OLS)
Fusha	Inferenca kauzale	Inferenca kauzale	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	2000	2009	2019
Krijuesi	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Wooldridge (textbook treatment); classical least squares
Lloji	Causal structure learning	Causal identification framework	Linear regression
Burimi themelues	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Emërtime të tjera	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Të lidhura	5	5	5
Përmbledhja	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.	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	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).