Localised Natural Causal Learning Algorithms for Weak Consistency Conditions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929358874083328 |
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| author | Teh, Kai Z Sadeghi, Kayvan Soo, Terry |
| author_facet | Teh, Kai Z Sadeghi, Kayvan Soo, Terry |
| contents | By relaxing conditions for natural structure learning algorithms, a family of constraint-based algorithms containing all exact structure learning algorithms under the faithfulness assumption, we define localised natural structure learning algorithms (LoNS). We also provide a set of necessary and sufficient assumptions for consistency of LoNS, which can be thought of as a strict relaxation of the restricted faithfulness assumption. We provide a practical LoNS algorithm that runs in exponential time, which is then compared with related existing structure learning algorithms, namely PC/SGS and the relatively recent sparsest permutation algorithm. Simulation studies are also provided. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_14775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Localised Natural Causal Learning Algorithms for Weak Consistency Conditions Teh, Kai Z Sadeghi, Kayvan Soo, Terry Methodology By relaxing conditions for natural structure learning algorithms, a family of constraint-based algorithms containing all exact structure learning algorithms under the faithfulness assumption, we define localised natural structure learning algorithms (LoNS). We also provide a set of necessary and sufficient assumptions for consistency of LoNS, which can be thought of as a strict relaxation of the restricted faithfulness assumption. We provide a practical LoNS algorithm that runs in exponential time, which is then compared with related existing structure learning algorithms, namely PC/SGS and the relatively recent sparsest permutation algorithm. Simulation studies are also provided. |
| title | Localised Natural Causal Learning Algorithms for Weak Consistency Conditions |
| topic | Methodology |
| url | https://arxiv.org/abs/2402.14775 |