Krahasoni metodat

	Rrjeti Bajesian	Identifikimi kauzal me Grafet Drejt-ciklike (do-calculus)
Fusha	Statistika bajesiane	Inferenca kauzale
Familja	Bayesian methods	Regression model
Viti i origjinës	1988	2009
Krijuesi	Judea Pearl	Judea Pearl
Lloji	Probabilistic graphical model	Causal identification framework
Burimi themelues	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
Emërtime të tjera	Bayes network, belief network, probabilistic graphical model, directed graphical model	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Të lidhura	4	5
Përmbledhja	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.