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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2402.14481 |
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| _version_ | 1866911782022414336 |
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| author | Biza, Konstantina Ntroumpogiannis, Antonios Triantafillou, Sofia Tsamardinos, Ioannis |
| author_facet | Biza, Konstantina Ntroumpogiannis, Antonios Triantafillou, Sofia Tsamardinos, Ioannis |
| contents | We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert human analyst would and answer a user's causal queries. We describe the architecture of such a platform, and illustrate its performance on synthetic data sets. As a case study, we apply it on temporal telecommunication data. The system is general and can be applied to a plethora of causal discovery problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Automated Causal Discovery: a case study on 5G telecommunication data Biza, Konstantina Ntroumpogiannis, Antonios Triantafillou, Sofia Tsamardinos, Ioannis Machine Learning Methodology We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert human analyst would and answer a user's causal queries. We describe the architecture of such a platform, and illustrate its performance on synthetic data sets. As a case study, we apply it on temporal telecommunication data. The system is general and can be applied to a plethora of causal discovery problems. |
| title | Towards Automated Causal Discovery: a case study on 5G telecommunication data |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.14481 |