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揭示因果关系的系统方法。这类方法可应用于众多研究领域，因而引起了学术界的广泛关注。机器学习模型通过从高维数据中提取相关模式，在各类任务中取得了成功，但在超出初始分布范围进行泛化时仍面临困难。由于因果引擎致力于学习与数据分布无关的机制，将机器学习与因果性相结合有望为两个领域带来共同增益。本研究首先阐明了这一假设并提供应用实例。我们首先从不同视角对因果性的理论与方法进行了全面综述，进而深入探究了因果性与机器学习之间的关联，并阐述了两个领域面临的挑战。我们展示了早期尝试将两个领域结合的研究成果，以及未来可能的发展方向。最后，我们提供了因果性技术在各类场景中的广泛应用案例。