قارن الطرق

	التعرف السببي باستخدام الرسوم البيانية الموجهة غير الدورية (حسابات do)	سلاسل ماركوف مونت كارلو (MCMC)
المجال	الاستدلال السببي	بايزي
العائلة	Regression model	Bayesian methods
سنة النشأة	2009	—
صاحب الطريقة	Judea Pearl	—
النوع	Causal identification framework	Posterior sampling algorithm
المصدر التأسيسي	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
الأسماء البديلة	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
ذات صلة	5	3
الملخص	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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.