方法对比

	GES算法	贝叶斯网络	NOTEARS：因果结构学习的连续优化
领域	因果推断	贝叶斯	因果推断
方法族	Machine learning	Bayesian methods	Machine learning
起源年份	2002	1988	2018
提出者	David Maxwell Chickering	Judea Pearl	Zheng, Aragam, Ravikumar & Xing
类型	Score-based causal structure learning algorithm	Probabilistic graphical model	Continuous optimization algorithm for causal DAG discovery
开创性文献	Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31.
别名	Greedy Equivalence Search, GES Causal Discovery, Score-Based Greedy Search, Açgözlü Eşdeğerlik Araması	Bayes network, belief network, probabilistic graphical model, directed graphical model	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
相关	2	4	1
摘要	Greedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering in 2002, GES operates directly on Markov equivalence classes of directed acyclic graphs (DAGs), represented as completed partially directed acyclic graphs (CPDAGs). Under the assumptions of causal sufficiency and a faithful data-generating process, GES is proven to recover the true equivalence class in the large-sample limit.	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.	NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization problem, enabling the use of standard gradient-based solvers and removing the need for exhaustive combinatorial search over graph space.