Salīdzināt metodes

	Algoritms GES	NOTEARS: Nepārtraukta optimizācija cēloņsakarību struktūras apguvei
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Machine learning	Machine learning
Izcelsmes gads	2002	2018
Autors	David Maxwell Chickering	Zheng, Aragam, Ravikumar & Xing
Tips	Score-based causal structure learning algorithm	Continuous optimization algorithm for causal DAG discovery
Pirmavots	Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.	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.
Citi nosaukumi	Greedy Equivalence Search, GES Causal Discovery, Score-Based Greedy Search, Açgözlü Eşdeğerlik Araması	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
Saistītās	2	1
Kopsavilkums	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.	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.