Compara mètodes

	Algorisme FCI	NOTEARS: Optimització Contínua per a l'Aprenentatge d'Estructura Causal
Camp	Inferència causal	Inferència causal
Família	Machine learning	Machine learning
Any d'origen	2000	2018
Autor original	Spirtes, Glymour & Scheines	Zheng, Aragam, Ravikumar & Xing
Tipus	Constraint-based causal discovery algorithm	Continuous optimization algorithm for causal DAG discovery
Font seminal	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0-262-19440-2	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.
Àlies	FCI, Fast Causal Inference, FCI Causal Discovery, FCI Algoritması	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
Relacionats	2	1
Resum	The Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, Prediction, and Search. Unlike its predecessor the PC algorithm, FCI is specifically designed to handle the presence of latent (unmeasured) common causes and sample selection bias. It outputs a Partial Ancestral Graph (PAG), which faithfully represents the set of all causal structures consistent with the observed conditional independencies.	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.