Cluster-Dags as Powerful Background Knowledge For Causal Discovery
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909994000056320 |
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| author | de Vargas, Jan Marco Ruiz Padh, Kirtan Kilbertus, Niki |
| author_facet | de Vargas, Jan Marco Ruiz Padh, Kirtan Kilbertus, Niki |
| contents | Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealing with high-dimensional data and complex dependencies. Incorporating prior knowledge about the system can aid causal discovery. In this work, we leverage Cluster-DAGs as a prior knowledge framework to warm-start causal discovery. We show that Cluster-DAGs offer greater flexibility than existing approaches based on tiered background knowledge and introduce two modified constraint-based algorithms, Cluster-PC and Cluster-FCI, for causal discovery in the fully and partially observed setting, respectively. Empirical evaluation on simulated data demonstrates that Cluster-PC and Cluster-FCI outperform their respective baselines without prior knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cluster-Dags as Powerful Background Knowledge For Causal Discovery de Vargas, Jan Marco Ruiz Padh, Kirtan Kilbertus, Niki Machine Learning Artificial Intelligence Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealing with high-dimensional data and complex dependencies. Incorporating prior knowledge about the system can aid causal discovery. In this work, we leverage Cluster-DAGs as a prior knowledge framework to warm-start causal discovery. We show that Cluster-DAGs offer greater flexibility than existing approaches based on tiered background knowledge and introduce two modified constraint-based algorithms, Cluster-PC and Cluster-FCI, for causal discovery in the fully and partially observed setting, respectively. Empirical evaluation on simulated data demonstrates that Cluster-PC and Cluster-FCI outperform their respective baselines without prior knowledge. |
| title | Cluster-Dags as Powerful Background Knowledge For Causal Discovery |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10032 |