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：在推荐系统中，用户行为数据是观测性而非实验性的，这导致数据中普遍存在偏差。因此，处理偏差已成为推荐系统领域的一项重大挑战。近年来，双重稳健学习因其卓越的性能和稳健特性而备受关注。然而，我们的实验结果表明，现有的双重学习方法受到所谓“有害归因”的严重影响——即当归因值严重偏离真实情况时，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，通过审查归因的均值和方差来筛选归因值。理论分析表明，CDR能够降低方差并改善尾部界。此外，我们的实验研究表明，CDR显著提升了性能，并能有效减少有害归因的出现频率。