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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.02495 |
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| _version_ | 1866916958563205120 |
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| author | Azua, Felipe Bertossi, Leopoldo |
| author_facet | Azua, Felipe Bertossi, Leopoldo |
| contents | The Causal Effect (CE) is a numerical measure of causal influence of variables on observed results. Despite being widely used in many areas, only preliminary attempts have been made to use CE as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score in the context of classical and probabilistic databases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Causal-Effect Score in Data Management Azua, Felipe Bertossi, Leopoldo Databases Artificial Intelligence The Causal Effect (CE) is a numerical measure of causal influence of variables on observed results. Despite being widely used in many areas, only preliminary attempts have been made to use CE as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score in the context of classical and probabilistic databases. |
| title | The Causal-Effect Score in Data Management |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2502.02495 |