قارن الطرق

	خوارزميات اكتشاف السببية (PC، FCI، LiNGAM)	التعرف السببي باستخدام الرسوم البيانية الموجهة غير الدورية (حسابات do)	تصميم الانحدار المقطوع (RDD)	المتغيرات الآلية عبر المربعات الصغرى ذات المرحلتين (IV/2SLS)
المجال	الاستدلال السببي	الاستدلال السببي	الاستدلال السببي	الاستدلال السببي
العائلة	Regression model	Regression model	Regression model	Regression model
سنة النشأة	2000	2009	2008	2009
صاحب الطريقة	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
النوع	Causal structure learning	Causal identification framework	Quasi-experimental causal design	Instrumental-variables regression
المصدر التأسيسي	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	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
الأسماء البديلة	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	RDD, regression discontinuity design, sharp RDD, fuzzy RDD	instrumental variables, IV estimation, 2SLS, instrumental variable regression
ذات صلة	5	5	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.	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.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).