The Causality field aims to find systematic methods for uncovering cause-effect relationships. Such methods can find applications in many research fields, justifying a great interest in this domain. Machine Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their initial distribution. As causal engines aim to learn mechanisms that are independent from a data distribution, combining Machine Learning with Causality has the potential to bring benefits to the two fields. In our work, we motivate this assumption and provide applications. We first perform an extensive overview of the theories and methods for Causality from different perspectives. We then provide a deeper look at the connections between Causality and Machine Learning and describe the challenges met by the two domains. We show the early attempts to bring the fields together and the possible perspectives for the future. We finish by providing a large variety of applications for techniques from Causality.

翻译：因果性领域旨在寻找系统性地揭示因果关系的方法。此类方法可应用于众多研究领域，因而在该领域引起了极大兴趣。机器学习模型通过从高维数据中提取相关模式，在大量任务中取得了成功，但在泛化到初始分布之外的数据时仍面临困难。由于因果引擎旨在学习独立于数据分布的机制，将机器学习与因果性相结合有望为这两个领域带来裨益。本文首先阐释了这一假设并提供了应用案例。我们首先从不同视角对因果性的理论与方法进行了广泛综述，进而深入探讨了因果性与机器学习之间的联系，并描述了这两个领域面临的挑战。我们展示了将两个领域结合的早期尝试，并展望了未来的可能方向。最后，我们提供了因果性技术在多领域中的广泛应用实例。