So sánh phương pháp

	Mạng Bayes	Nhận dạng nhân quả với Đồ thị có hướng không chu trình (do-calculus)
Lĩnh vực	Bayes	Suy luận nhân quả
Họ	Bayesian methods	Regression model
Năm ra đời	1988	2009
Người khởi xướng	Judea Pearl	Judea Pearl
Loại	Probabilistic graphical model	Causal identification framework
Công trình gốc	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
Tên gọi khác	Bayes network, belief network, probabilistic graphical model, directed graphical model	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Liên quan	4	5
Tóm tắt	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.