On scoring maximal ancestral graphs with the max-min hill climbing algorithm

DOI10.1016/j.ijar.2018.08.002zbMath1448.68378OpenAlexW2885495226WikidataQ129399442 ScholiaQ129399442MaRDI QIDQ1726268

Sofia Triantafillou, Vincenzo Lagani, Konstantinos Tsirlis, Ioannis Tsamardinos

Publication date: 20 February 2019

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2018.08.002

zbMATH Keywords

maximal ancestral graphshybrid causal structure learningmax-min hill climbing

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (4)

Causal structure learning: a combinatorial perspective ⋮ Discovering causal graphs with cycles and latent confounders: an exact branch-and-bound approach ⋮ Unnamed Item ⋮ Effective and efficient structure learning with pruning and model averaging strategies

Uses Software

• TETRAD
• Hailfinder

Cites Work

• Unnamed Item
• Unnamed Item
• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• Adaptive probabilistic networks with hidden variables
• Ancestral graph Markov models.
• On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
• Estimating bounds on causal effects in high-dimensional and possibly confounded systems
• Structural Intervention Distance for Evaluating Causal Graphs

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