BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

@inproceedings{Rothenhusler2015BACKSHIFTLC,
  title={BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions},
  author={Dominik Rothenh{\"a}usler and Christina Heinze and Jonas Peters and Nicolai Meinshausen},
  booktitle={NIPS},
  year={2015}
}
We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific kind of interventions (“shift interventions”). The location and strength of these interventions do not have to be known and can be estimated from the data. Our method, called BACKSHIFT, only uses second moments of the data and performs simple joint matrix diagonalization, applied to differences between covariance… CONTINUE READING