Learning high-dimensional directed acyclic graphs with latent and selection variables

@inproceedings{Colombo2012LearningHD,
  title={Learning high-dimensional directed acyclic graphs with latent and selection variables},
  author={Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
  year={2012}
}
We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore propose the new RFCI algorithm, which is much faster than FCI. In some situations the output of RFCI… CONTINUE READING

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