Tagged for Direction: Pinning Down Causal Edge Directions with Precision
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912446855249920 |
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| author | Busch, Florian Peter Willig, Moritz Guldan, Florian Kersting, Kristian Dhami, Devendra Singh |
| author_facet | Busch, Florian Peter Willig, Moritz Guldan, Florian Kersting, Kristian Dhami, Devendra Singh |
| contents | Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type assignments induce a preference on the causal direction of other pairs of variables with the same type. Although useful, this assignment of a specific type to a variable can be tricky in practice. We propose a tag-based causal discovery approach where multiple tags are assigned to each variable in a causal graph. Existing causal discovery approaches are first applied to direct some edges, which are then used to determine edge relations between tags. Then, these edge relations are used to direct the undirected edges. Doing so improves upon purely type-based relations, where the assumption of type consistency lacks robustness and flexibility due to being restricted to single types for each variable. Our experimental evaluations show that this boosts causal discovery and that these high-level tag relations fit common knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tagged for Direction: Pinning Down Causal Edge Directions with Precision Busch, Florian Peter Willig, Moritz Guldan, Florian Kersting, Kristian Dhami, Devendra Singh Machine Learning Artificial Intelligence Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type assignments induce a preference on the causal direction of other pairs of variables with the same type. Although useful, this assignment of a specific type to a variable can be tricky in practice. We propose a tag-based causal discovery approach where multiple tags are assigned to each variable in a causal graph. Existing causal discovery approaches are first applied to direct some edges, which are then used to determine edge relations between tags. Then, these edge relations are used to direct the undirected edges. Doing so improves upon purely type-based relations, where the assumption of type consistency lacks robustness and flexibility due to being restricted to single types for each variable. Our experimental evaluations show that this boosts causal discovery and that these high-level tag relations fit common knowledge. |
| title | Tagged for Direction: Pinning Down Causal Edge Directions with Precision |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19459 |