  Recent years have seen rapid progress at the intersection between causality
and machine learning. 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
convolutional 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 underlying 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.
