# 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

#### One Citation

Estimating the probabilities of causation via deep monotonic twin networks

- Computer Science
- 2021

There has been much recent work using machine learning to answer causal queries. Most focus on interventional queries, such as the conditional average treatment effect. However, as noted by Pearl,… Expand

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