108,631 research outputs found
Deep Learning of Causal Structures in High Dimensions
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
- …