Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach

DOI10.1016/j.csda.2014.03.003zbMath1506.62184OpenAlexW2014887862MaRDI QIDQ1623596

Qiang Zhao, Zhi Geng, Changzhang Wang, You Zhou

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.03.003

zbMATH Keywords

directed acyclic graphcausal networkdiscover causes and effectssequential structural learning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Bayesian network based label correlation analysis for multi-label classifier chain ⋮ A local method for identifying causal relations under Markov equivalence ⋮ Local causal structure learning for streaming features ⋮ Unnamed Item

Uses Software

• TETRAD
• Hailfinder

Cites Work

• The max-min hill-climbing Bayesian network structure learning algorithm
• Towards scalable and data efficient learning of Markov boundaries
• Causation, prediction, and search
• Adaptive probabilistic networks with hidden variables
• Learning Bayesian networks: The combination of knowledge and statistical data
• Decomposition of structural learning about directed acyclic graphs
• Tabu Search-Enhanced Graphical Models for Classification in High Dimensions
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