مقایسهٔ روش‌ها

	الگوریتم‌های کشف علّی (PC, FCI, LiNGAM)	شناسایی علّی با استفاده از گراف‌های جهت‌دار بدون دور (حساب do)	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	استنتاج علّی	استنتاج علّی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	2000	2009	2019
پدیدآور	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Wooldridge (textbook treatment); classical least squares
نوع	Causal structure learning	Causal identification framework	Linear regression
منبع بنیادین	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
نام‌های دیگر	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
مرتبط	5	5	5
خلاصه	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).