Deep Learning of Causal Structures in High Dimensions

2022/12/09 by Lagemann, Kai, Lagemann, Christian, Taschler, Bernd +1 · 3 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2212.04866

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.

Cited by

• Noise-robust Contrastive Learning for Critical Transition Detection in Dynamical Systems
• Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
• DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

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