Causal Structure Learning: a Bayesian approach based on random graphs
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2020
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| _version_ | 1866908999124779008 |
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| author | Gonzalez-Soto, Mauricio Feliciano-Avelino, Ivan R. Sucar, L. Enrique Balderas, Hugo J. Escalante |
| author_facet | Gonzalez-Soto, Mauricio Feliciano-Avelino, Ivan R. Sucar, L. Enrique Balderas, Hugo J. Escalante |
| contents | A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_06164 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Causal Structure Learning: a Bayesian approach based on random graphs Gonzalez-Soto, Mauricio Feliciano-Avelino, Ivan R. Sucar, L. Enrique Balderas, Hugo J. Escalante Artificial Intelligence A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures. |
| title | Causal Structure Learning: a Bayesian approach based on random graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2010.06164 |