Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.

翻译：因果关系已成为解释各研究领域中事件、现象及结果之间关系的基本方法。它已渗透至医学、医疗保健、经济学、金融、欺诈检测、网络安全、教育、公共政策、推荐系统、异常检测、机器人学、控制论、社会学、市场营销及广告等多个领域与应用方向。本文通过对过去五十年间因果关系发展的梳理，阐明其与其他方法的差异及使用前提。此外，论文展示了因果关系如何与人工智能（AI）、生成式人工智能（GAI）、机器与深度学习、强化学习（RL）及模糊逻辑等新兴方法相互作用。我们研究了因果关系对各领域的影响、贡献及其与前沿方法的交互机制。同时，论文通过实例论证了因果模型的可信度与可解释性。我们提供了多种评估因果模型的方法，并探讨了未来发展方向。