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Towards Causal Representation Learning
[article]
2021
arXiv
pre-print
The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, 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
arXiv:2102.11107v1
fatcat:n25xwac72nfulgl3gvvs4kerca