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Paper "Relating Graph Neural Networks to Structural Causal Models": https://arxiv.org/pdf/2109.04173.pdf

Abstract: Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of interest the underlying SCM will only be partially observable, thus causal inference tries to leverage any exposed information. Graph neural networks (GNN) as universal approximators on structured input pose a viable candidate for causal learning, suggesting a tighter integration with SCM. To this effect we present a theoretical analysis from first principles that establishes a novel connection between GNN and SCM while providing an extended view on general neuralcausal models. We then establish a new model class for GNNbased causal inference that is necessary and sufficient for causal effect identification. Our empirical illustration on simulations and standard benchmarks validate our theoretical proofs.

Authors: Matej Zečević, Devendra Singh Dhami, Petar Veličković, Kristian Kersting

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00:00 Introduction
01:13 Towards NeuroCausality intro
25:28 GNNBased Causal Inference
48:35 Identifiability & Estimation
59:55 Causality for Machine Learning
1:04:20 Q&A