In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation.

翻译：摘要：在推荐系统中，用户行为数据为观测数据而非实验数据，导致数据中存在广泛偏差。因此，消除偏差已成为推荐系统领域的主要挑战之一。近年来，双重稳健学习因其卓越性能和鲁棒特性备受关注。然而，我们的实验结果表明，现有双重稳健方法受到所谓“有害插补”的严重影响——当插补值显著偏离真实值时，反而会产生反效果。为解决此问题，本文提出保守双重稳健策略，通过严格审查插补值的均值与方差进行过滤。理论分析表明，保守双重稳健策略可降低方差并改善尾部边界。此外，实验研究证明，保守双重稳健策略能显著提升性能，并有效减少有害插补的发生频率。