ExMAG: Learning of Maximally Ancestral Graphs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915297414348800 |
|---|---|
| author | Ryšavý, Petr Rytíř, Pavel He, Xiaoyu Korpas, Georgios Mareček, Jakub |
| author_facet | Ryšavý, Petr Rytíř, Pavel He, Xiaoyu Korpas, Georgios Mareček, Jakub |
| contents | In mixed graphs, there are both directed and undirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of considerable interest in causal learning in the presence of confounders. There, directed edges represent a clear direction of causality, while undirected edges represent confounding. We propose a score-based branch-and-cut algorithm for learning maximally ancestral graphs. The algorithm produces more accurate results than state-of-the-art methods, while being faster to run on small and medium-sized synthetic instances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08245 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ExMAG: Learning of Maximally Ancestral Graphs Ryšavý, Petr Rytíř, Pavel He, Xiaoyu Korpas, Georgios Mareček, Jakub Machine Learning In mixed graphs, there are both directed and undirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of considerable interest in causal learning in the presence of confounders. There, directed edges represent a clear direction of causality, while undirected edges represent confounding. We propose a score-based branch-and-cut algorithm for learning maximally ancestral graphs. The algorithm produces more accurate results than state-of-the-art methods, while being faster to run on small and medium-sized synthetic instances. |
| title | ExMAG: Learning of Maximally Ancestral Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.08245 |