The Importance of Causality in Decision Making: A Perspective on Recommender Systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 66 of its 176 references that are in the map. First posted on arXiv 16 Sep 2024.
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66 of 176 references.
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38 more
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