Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.

翻译：因果性与可解释人工智能（XAI）作为计算机科学中各自独立发展的领域，尽管因果与解释的基本概念共享古老的共同根源。这种分离因缺乏同时涵盖这两个领域的综述性研究而进一步加剧。本文通过文献调研，试图理解因果性与XAI在何种程度及以何种方式相互交织。具体而言，我们旨在揭示这两个概念之间存在的关联类型，以及如何从中受益（例如，在构建对AI系统的信任方面）。由此，我们识别出三种主要视角：第一种视角认为缺乏因果性是当前AI与XAI方法的主要局限之一，并探讨了"最优"解释形式；第二种视角采取实用主义观点，将XAI视为通过识别值得追踪的实验操作来促进因果探究的科学探索工具；第三种视角则支持因果性以三种方式成为XAI的预备性基础：利用源自因果性的概念支持或改进XAI，运用反事实推理实现可解释性，以及将获取因果模型本身视为解释。为补充分析，我们还提供了用于自动化因果任务的相关软件解决方案。我们认为，本研究通过突出领域间的潜在桥梁并揭示可能存在的局限，为因果性与XAI这两个领域提供了统一视角。