Seminar Geometry, Algebra and Physics in Deep Neural Networks

Abstract: The field of causality has recently emerged as a subject of interest in machine learning, largely due to major advances in data collection methods in the biological sciences and tech industries where large-scale observational and experimental data sets can now be efficiently and ethically obtained. The modern approach to causality decomposes the inference process into two fundamental problems: the inference of causal relations between variables in a complex system and the estimation of the causal effect of one variable on another given that such a relation exists. The subject of this talk will be the former of the two problems, commonly called causal discovery, where the aim is to learn a complex causal network from the available data. We will give a soft introduction to the basics of causal modeling and causal discovery, highlighting where combinatorics and geometry have already started to contribute.

Going deeper, we will analyze how and when geometry and combinatorics help us identify causal structure without the use of experimental data.