Recommender systems often suffer from selection bias as users tend to rate their preferred items. The datasets collected under such conditions exhibit entries missing not at random and thus are not randomized-controlled trials representing the target population. To address this challenge, a doubly robust estimator and its enhanced variants have been proposed as they ensure unbiasedness when accurate imputed errors or predicted propensities are provided. However, we argue that existing estimators rely on miscalibrated imputed errors and propensity scores as they depend on rudimentary models for estimation. We provide theoretical insights into how miscalibrated imputation and propensity models may limit the effectiveness of doubly robust estimators and validate our theorems using real-world datasets. On this basis, we propose a Doubly Calibrated Estimator that involves the calibration of both the imputation and propensity models. To achieve this, we introduce calibration experts that consider different logit distributions across users. Moreover, we devise a tri-level joint learning framework, allowing the simultaneous optimization of calibration experts alongside prediction and imputation models. Through extensive experiments on real-world datasets, we demonstrate the superiority of the Doubly Calibrated Estimator in the context of debiased recommendation tasks.

翻译：推荐系统常受选择偏差影响，因用户倾向于对偏好项目进行评分。在此条件下收集的数据集存在非随机缺失条目，因而并非代表目标总体的随机对照试验。为应对这一挑战，研究者提出了双重稳健估计器及其增强变体，这些方法可在提供准确插补误差或预测倾向性时确保无偏性。然而，我们认为现有估计器依赖于未校准的插补误差与倾向性分数——其通过基础模型进行估计。我们提供了理论洞见，阐明未校准的插补模型与倾向性模型如何限制双重稳健估计器的有效性，并通过真实数据集验证了相关定理。在此基础上，我们提出双重校准估计器，其需同时校准插补模型与倾向性模型。为达成此目标，我们引入了考虑用户间不同对数几率分布的校准专家机制。此外，我们设计了三层级联联合学习框架，允许校准专家与预测模型、插补模型实现同步优化。通过在真实数据集上的大量实验，我们证明了双重校准估计器在去偏推荐任务中的优越性。