The Importance of Causality in Decision Making: A Perspective on Recommender Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917793287372800 |
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| author | Cavenaghi, Emanuele Zanga, Alessio Stella, Fabio Zanker, Markus |
| author_facet | Cavenaghi, Emanuele Zanga, Alessio Stella, Fabio Zanker, Markus |
| contents | Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS literature has repeatedly highlighted that, in real-world scenarios, recommendation algorithms suffer many types of biases since assumptions ensuring unbiasedness are likely not met. In this discussion paper, we formulate the RS problem in terms of causality, using potential outcomes and structural causal models, by giving formal definitions of the causal quantities to be estimated and a general causal graph to serve as a reference to foster future research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01822 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Importance of Causality in Decision Making: A Perspective on Recommender Systems Cavenaghi, Emanuele Zanga, Alessio Stella, Fabio Zanker, Markus Information Retrieval Artificial Intelligence Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate predictions into effective and explainable decisions. Indeed, the RS literature has repeatedly highlighted that, in real-world scenarios, recommendation algorithms suffer many types of biases since assumptions ensuring unbiasedness are likely not met. In this discussion paper, we formulate the RS problem in terms of causality, using potential outcomes and structural causal models, by giving formal definitions of the causal quantities to be estimated and a general causal graph to serve as a reference to foster future research and development. |
| title | The Importance of Causality in Decision Making: A Perspective on Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01822 |