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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.02535 |
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| _version_ | 1866916930207612928 |
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| author | Laurentino, Eduardo Rocha Cozman, Fabio Gagliardi Maua, Denis Deratani Lawand, Daniel Angelo Esteves Coelho, Davi Goncalves Bezerra Marques, Lucas Martins |
| author_facet | Laurentino, Eduardo Rocha Cozman, Fabio Gagliardi Maua, Denis Deratani Lawand, Daniel Angelo Esteves Coelho, Davi Goncalves Bezerra Marques, Lucas Martins |
| contents | Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models Laurentino, Eduardo Rocha Cozman, Fabio Gagliardi Maua, Denis Deratani Lawand, Daniel Angelo Esteves Coelho, Davi Goncalves Bezerra Marques, Lucas Martins Machine Learning Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths. |
| title | Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.02535 |