用因果模型解决推荐系统中的偏差问题，提升决策可解释性。
The Importance of Causality in Decision Making: A Perspective on Recommender Systems
- 基于潜在结果与因果图建模推荐问题
- 明确需估计的因果效应量，统一研究框架
- 适合关注推荐系统公平性与可解释性的研究者
因果推理在推荐系统领域日益受到重视，因为推荐系统若能引入因果分析，便有望将精准预测转化为有效且可解释的决策。现实中，推荐算法常因违背无偏假设而产生多种偏差。本文从因果视角重新定义推荐问题，采用潜在结果和结构因果模型，给出因果量的正式定义，并构建通用因果图作为未来研究的参考框架。
原文摘要 · Abstract (English)
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.
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