Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work.
翻译:近年来,由于因果关系揭示了各种事物之间的基本关系,并表明世界的进展如何,因此,由于因果关系近年来引起了许多关注,因此,因果关系学习近年来引起了许多问题和瓶颈,然而,传统因果学习方法中存在许多问题和瓶颈,例如高维的无结构变量、组合优化问题、未知的干预、未观察到的困惑者、选择偏向和估计偏向。深因学习,即基于深厚神经网络的因果学习,为解决这些问题带来了新的见解。虽然已经提出了许多深层次的基于学习的因果发现和因果推断方法,但缺乏对内部深层次学习机制的探讨,以改进因果学习。在本篇文章中,我们全面审查深层次的学习如何有助于因果学习,从三个方面应对常规挑战:陈述、发现和推断。我们指出,深因果学习对于因果科学的理论扩展和应用十分重要,也是一般人工智能不可或缺的部分。我们最后的文章总结了公开的问题和今后工作的潜在方向。