Deep Learning of Causal Structures in High Dimensions

Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

Abstract:

Recent years have seen rapid progress at the intersection between causality and machine learn-
ing. Motivated by scientific applications involving high-dimensional data, in particular in
biomedicine, we propose a deep neural architecture for learning causal relationships between
variables from a combination of empirical data and prior causal knowledge. We combine con-
volutional and graph neural networks within a causal risk framework to provide a flexible and
scalable approach. Empirical results include linear and nonlinear simulations (where the un-
derlying causal structures are known and can be directly compared against), as well as a real
biological example where the models are applied to high-dimensional molecular data and their
output compared against entirely unseen validation experiments. These results demonstrate the
feasibility of using deep learning approaches to learn causal networks in large-scale problems
spanning thousands of variables.