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Context-Specific Causal Discovery for Categorical Data Using Staged Trees

This repo contatins the file and code to replicate the results in:

Manuele Leonelli and Gherardo Varando, Context-Specific Causal Discovery for Categorical Data Using Staged Trees Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:8871-8888, 2023.

@InProceedings{pmlr-v206-leonelli23a,
  title = 	 {Context-Specific Causal Discovery for Categorical Data Using Staged Trees},
  author =       {Leonelli, Manuele and Varando, Gherardo},
  booktitle = 	 {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {8871--8888},
  year = 	 {2023},
  editor = 	 {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
  volume = 	 {206},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v206/leonelli23a/leonelli23a.pdf},
  url = 	 {https://proceedings.mlr.press/v206/leonelli23a.html}
}

The code is written in R and based mainly in the stagedtrees package available on CRAN and on github.

Additional needed package, all avialable from CRAN:

  • bnlearn
  • SID
  • ggplot2
  • data.table
  • plyr
  • ncdf4
  • pracma

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