Relating Graph Neural Networks to Structural Causal Models

Understand

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 leveraging the exposed.
• 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 more general view on neural-causal models, revealing several novel connections between GNN and SCM.