Introduction to Causality
|Lecturer:||Bernhard Schölkopf (MPI-IS)|
|Time:||– (Zurich time)|
|Slides:||Click here to download!|
|Recording:||Click here to view! (only for ETH members)|
Abstract: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 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, the discovery of high-level causal variables from low-level observations.
- Schölkopf, B. et al. (2021). Towards causal representation learning. arXiv:2102.11107.