Módszerek összehasonlítása

	FCI Algoritmus	Bayes-háló	NOTEARS: Optimalisálás folytonos változókkal az ok-okozati struktúra tanulásához
Tudományterület	Oksági következtetés	Bayes-statisztika	Oksági következtetés
Módszercsalád	Machine learning	Bayesian methods	Machine learning
Keletkezés éve	2000	1988	2018
Megalkotó	Spirtes, Glymour & Scheines	Judea Pearl	Zheng, Aragam, Ravikumar & Xing
Típus	Constraint-based causal discovery algorithm	Probabilistic graphical model	Continuous optimization algorithm for causal DAG discovery
Alapmű	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0-262-19440-2	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.
Alternatív nevek	FCI, Fast Causal Inference, FCI Causal Discovery, FCI Algoritması	Bayes network, belief network, probabilistic graphical model, directed graphical model	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
Kapcsolódó	2	4	1
Összefoglaló	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.	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.