The field of generating recommendations within the framework of causal inference has seen a recent surge, with recommendations being likened to treatments. This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often leveraged propensity scores to mitigate bias, albeit at the risk of introducing additional variance. Others have explored the use of unbiased data from randomized controlled trials, although this comes with assumptions that may prove challenging in practice. In this paper, we first present the causality-aware interpretation of recommendations and reveal how the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set. Building on this foundation, we present a novel contrastive counterfactual learning method (CCL) that incorporates three unique positive sampling strategies grounded in estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.

翻译：在因果推断框架下生成推荐的研究领域近期出现了激增，其中推荐被类比为干预措施。这种方法增强了对推荐如何影响用户行为的洞察，并有助于识别潜在因素。现有研究常利用倾向得分来减轻偏差，但可能引入额外方差。另一些研究则利用随机对照试验的无偏数据，但这一方法依赖于实践中可能难以满足的假设。本文首先提出因果感知的推荐解释，并揭示潜在暴露机制如何使观测反馈的最大似然估计产生偏差。鉴于混杂因子可能难以捉摸，我们提出一种对比自监督学习来最小化暴露偏差，采用逆倾向得分并扩展正样本集。在此基础上，我们提出一种新颖的对比反事实学习方法（CCL），该方法基于估计的暴露概率或随机反事实样本，融合了三种独特的正采样策略。通过在两个真实数据集上的广泛实验，我们证明提出的CCL方法优于现有最先进方法。