विधियों की तुलना करें

	DAG Causal Identification	मार्कोव चेन मोंटे कार्लो (MCMC)
क्षेत्र	कारणात्मक अनुमान	बायेसियन
परिवार	Regression model	Bayesian methods
उद्भव वर्ष	2009	—
प्रवर्तक	Judea Pearl	—
प्रकार	Causal identification framework	Posterior sampling algorithm
मौलिक स्रोत	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
उपनाम	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
संबंधित	5	3
सारांश	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.