Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.

翻译：建议系统旨在预测用户对未接触的物品的反馈,由于存在一些无法计量的变量(例如用户的社会经济地位),从而影响到用户的接触和反馈,因此产生了令人不安的偏见。现有方法:(1)对这些未计量的变量作出站不住脚的假设,或者(2)直接推断潜在潜在混淆者与用户的接触。然而,这些方法无法保证查明反事实反馈,从而可能导致有偏颇的预测。在这项工作中,我们提出一种新颖的方法,即可识别的分解变量(iDCF),利用一套替代变量(例如观测到的用户特征)来解决上述非身份认同问题。拟议的iDCF是一个一般性的、无根据的建议框架,它应用准因果推论推断未计量的混淆者,并用理论保证来确定反事实反馈。在各种真实世界和合成数据集上进行的广泛实验,以核实拟议方法的有效性和稳健性。