• Corpus ID: 244798694

Inducing Causal Structure for Interpretable Neural Networks

@article{Geiger2021InducingCS,
  title={Inducing Causal Structure for Interpretable Neural Networks},
  author={Atticus Geiger and Zhengxuan Wu and Hanson Lu and Josh Rozner and Elisa Kreiss and Thomas F. Icard and Noah D. Goodman and Christopher Potts},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.00826}
}
In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashion. To achieve this, we present the new method of interchange intervention training (IIT). In IIT, we (1) align variables in the causal model with representations in the neural model and (2) train a neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in… 

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