Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea is to achieve model fairness from a causal perspective. The remaining challenge is that we have no access to interventions, i.e., the counterfactual world that produces recommendations when each user have changed the sensitive attribute value. To this end, we first borrow the Rubin-Neyman potential outcome framework to define average causal effects of sensitive attributes. Then, we show that removing causal effects of sensitive attributes is equal to average counterfactual fairness in CF. Then, we use the propensity re-weighting paradigm to estimate the average causal effects of sensitive attributes and formulate the estimated causal effects as an additional regularization term. To the best of our knowledge, we are one of the first few attempts to achieve counterfactual fairness from the causal effect estimation perspective in CF, which frees us from building sophisticated causal graph. Finally, experiments on three real-world datasets show the superiority of our proposed model.

最近，协同过滤（CF）算法中的用户方公平性问题引起了相当多的关注，认为结果不应基于用户的敏感属性（例如性别）来歧视个人或子用户组。研究人员通过减少预测和敏感属性之间的统计关联，提出了具有公平意识的 CF 模型。更自然的想法是从因果角度实现模型公平性。剩下的挑战是我们无法进行干预，即当每个用户更改敏感属性值时产生建议的反事实世界。为此，我们首先借用Rubin-Neyman潜在结果框架来定义敏感属性的平均因果效应。然后，我们证明消除敏感属性的因果影响等于 CF 中的平均反事实公平性。然后，我们使用倾向重新加权范式来估计敏感属性的平均因果效应，并将估计的因果效应公式化为附加正则化项。据我们所知，我们是最早从 CF 中因果效应估计角度实现反事实公平性的尝试之一，这使我们无需构建复杂的因果图。最后，对三个现实世界数据集的实验表明了我们提出的模型的优越性。