Greedy Causal Discovery Is Geometric

DOI10.1137/21M1457205MaRDI QIDQ5883283

Liam Solus, Petter Restadh, Svante Linusson

Publication date: 30 March 2023

Published in: SIAM Journal on Discrete Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.03771

zbMATH Keywords

polytopeBayesian networkgraphical modelcausal discoverycharacteristic imsetGESedge-walkmax-min Hill climbing

Mathematics Subject Classification ID

Combinatorial properties of polytopes and polyhedra (number of faces, shortest paths, etc.) (52B05) Special polytopes (linear programming, centrally symmetric, etc.) (52B12) Probabilistic graphical models (62H22) Causal inference from observational studies (62D20)

Related Items (2)

Causal structure learning: a combinatorial perspective ⋮ Algebraic structures in statistical methodology. Abstracts from the workshop held December 4--10, 2022

Uses Software

• polymake
• R
• MASS (R)
• pcalg
• GitHub

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The max-min hill-climbing Bayesian network structure learning algorithm
• A characterization of Markov equivalence classes for acyclic digraphs
• Computing convex hulls and counting integer points with \texttt{polymake}
• Towards using the chordal graph polytope in learning decomposable models
• Statistical Analysis of Financial Data in S-Plus
• Lectures on Polytopes
• Generalized Permutohedra from Probabilistic Graphical Models
• 10.1162/153244303321897717
• Consistency guarantees for greedy permutation-based causal inference algorithms
• Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs

This page was built for publication: Greedy Causal Discovery Is Geometric