Deep Learning of Causal Structures in High Dimensions
- URL: http://arxiv.org/abs/2212.04866v1
- Date: Fri, 9 Dec 2022 14:12:47 GMT
- Title: Deep Learning of Causal Structures in High Dimensions
- Authors: Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee
- Abstract summary: 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.
- Score: 0.6021787236982659
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
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