Recommender Systems (RS) have significantly advanced online content discovery and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite numerous progress on TRS, most of them focus on data correlations while overlooking the fundamental causal nature in recommendation. This drawback hinders TRS from identifying the cause in addressing trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noises while offering insightful explanations for TRS. However, there lacks a timely survey in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.

翻译：推荐系统（RS）显著推动了在线内容发现与个性化决策的发展。然而，推荐系统中新出现的脆弱性正促使范式向可信推荐系统（TRS）转变。尽管TRS研究取得诸多进展，但多数方法聚焦于数据关联性，忽视了推荐过程中根本的因果本质。这一缺陷阻碍了TRS在解决可信性问题时识别根本原因，导致公平性、鲁棒性和可解释性受限。为填补这一空白，因果学习作为一类有前景的方法涌现出来以增强TRS。这些基于可靠因果性的方法擅长减轻各类偏差与噪声，同时为TRS提供富有洞见的解释。然而，这一活跃领域尚缺乏及时的综述。本文从因果学习视角对TRS进行系统性概述。我们首先阐述面向因果的TRS（CTRS）的优势与通用流程，继而识别各阶段潜在的可信性挑战并将其与可行的因果解决方案相关联，随后对CTRS方法进行分类。最后，我们探讨推动该领域发展的若干未来方向。