Corpus ID: 211029267

Copy, paste, infer: A robust analysis of twin networks for counterfactual inference

@inproceedings{Graham2019CopyPI,
  title={Copy, paste, infer: A robust analysis of twin networks for counterfactual inference},
  author={L. Graham and Ciar{\'a}n M. Lee},
  year={2019}
}
Twin networks are a simple method for estimating counterfactuals, originally proposed to have several advantages over standard counterfactual inference. However, no study yet exists exploring in what contexts twin networks would be more advantageous than standard counterfactual methods in practice. We conduct an empirical and theoretical analysis of twin networks to show that in certain cases of Structural Causal Models, twin networks are faster and less memory intensive by orders of magnitude… Expand

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Estimating the probabilities of causation via deep monotonic twin networks
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