विधियों की तुलना करें

	बेयसियन नेटवर्क	DAG Causal Identification
क्षेत्र	बायेसियन	कारणात्मक अनुमान
परिवार	Bayesian methods	Regression model
उद्भव वर्ष	1988	2009
प्रवर्तक	Judea Pearl	Judea Pearl
प्रकार	Probabilistic graphical model	Causal identification framework
मौलिक स्रोत	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
उपनाम	Bayes network, belief network, probabilistic graphical model, directed graphical model	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
संबंधित	4	5
सारांश	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	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.