• Corpus ID: 237454612

Relating Graph Neural Networks to Structural Causal Models

@article{Zecevic2021RelatingGN,
  title={Relating Graph Neural Networks to Structural Causal Models},
  author={M. Zecevic and Devendra Singh Dhami and Petar Velickovic and Kristian Kersting},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.04173}
}
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… 

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