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Toward Causal Representation Learning
Proceedings of the IEEE ( IF 10.252 ) Pub Date : 2021-02-26 , DOI: 10.1109/jproc.2021.3058954
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.

中文翻译:

走向因果表征学习

机器学习和图形因果关系这两个领域应运而生,并分别进行了开发。但是,如今,在这两个领域中都有异花传粉和越来越高的兴趣,可以从另一个领域的进步中受益。在本文中,我们回顾了因果推理的基本概念,并将它们与机器学习的关键开放问题(包括传递和泛化)相关联,从而分析了因果关系如何有助于现代机器学习研究。这也适用于相反的方向:我们注意到大多数因果关系的工作都始于给出因果变量的前提。因此,AI和因果关系的中心问题是因果表示学习,即从低层观察中发现高层因果变量。最后,
更新日期:2021-05-04
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