Estimating large causal polytrees from small samples
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866914914262581248 |
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| author | Chatterjee, Sourav Vidyasagar, Mathukumalli |
| author_facet | Chatterjee, Sourav Vidyasagar, Mathukumalli |
| contents | We consider the problem of estimating a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in gene regulatory networks. We give an algorithm that recovers the tree with high accuracy in such settings. The algorithm works under essentially no distributional or modeling assumptions other than some mild non-degeneracy conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_07028 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Estimating large causal polytrees from small samples Chatterjee, Sourav Vidyasagar, Mathukumalli Methodology Machine Learning Probability Statistics Theory 62D20 We consider the problem of estimating a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in gene regulatory networks. We give an algorithm that recovers the tree with high accuracy in such settings. The algorithm works under essentially no distributional or modeling assumptions other than some mild non-degeneracy conditions. |
| title | Estimating large causal polytrees from small samples |
| topic | Methodology Machine Learning Probability Statistics Theory 62D20 |
| url | https://arxiv.org/abs/2209.07028 |