Deep 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 independent from a data distribution, combining Deep Learning with Causality can have a great impact on the two fields. In this paper, we further motivate this assumption. We perform an extensive overview of the theories and methods for Causality from different perspectives, with an emphasis on Deep Learning and the challenges met by the two domains. We show 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：深度学习模型通过从高维数据中提取相关性模式，在众多任务中取得了成功，但在泛化至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，将深度学习与因果关系相结合可能对这两个领域产生重大影响。本文进一步论证了这一假设，并从不同角度对因果关系的理论与方法进行了全面综述，重点探讨了深度学习以及这两个领域面临的挑战。我们展示了将两者结合的早期尝试，并展望了未来的可能方向。最后，我们列举了因果关系技术在众多领域中的应用。