Boosting Local Causal Discovery in High-Dimensional Expression Data

@article{Versteeg2019BoostingLC,
  title={Boosting Local Causal Discovery in High-Dimensional Expression Data},
  author={Philip Versteeg and Joris M. Mooij},
  journal={2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  year={2019},
  pages={2599-2604}
}
  • Philip Versteeg, Joris M. Mooij
  • Published in
    IEEE International Conference…
    2019
  • Computer Science, Mathematics
  • We study the performance of Local Causal Discovery (LCD) [5], a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm [13], we use an optional preselection method and two different statistical tests. Empirically, the resulting LCD estimator is seen to closely approach the accuracy of ICP, the state-of-the-art… CONTINUE READING

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