เปรียบเทียบวิธี

	การระบุสาเหตุด้วยกราฟอะไซคลิกแบบมีทิศทาง (do-calculus)	Markov Chain Monte Carlo (MCMC)
สาขาวิชา	การอนุมานเชิงสาเหตุ	เบย์
ตระกูล	Regression model	Bayesian methods
ปีกำเนิด	2009	—
ผู้ริเริ่ม	Judea Pearl	—
ประเภท	Causal identification framework	Posterior sampling algorithm
แหล่งต้นตำรับ	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
ชื่อเรียกอื่น	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
ที่เกี่ยวข้อง	5	3
สรุป	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.