পদ্ধতির তুলনা করুন

	FCI অ্যালগরিদম	নটিয়ার্স (NOTEARS): কার্যকারণ কাঠামোর শিক্ষণের জন্য অবিচ্ছিন্ন অপ্টিমাইজেশন
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000	2018
প্রবর্তক	Spirtes, Glymour & Scheines	Zheng, Aragam, Ravikumar & Xing
ধরন	Constraint-based causal discovery algorithm	Continuous optimization algorithm for causal DAG discovery
মৌলিক উৎস	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.
অপর নাম	FCI, Fast Causal Inference, FCI Causal Discovery, FCI Algoritması	DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi
সম্পর্কিত	2	1
সারসংক্ষেপ	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.