Localised Natural Causal Learning Algorithms for Weak Consistency Conditions

@InProceedings{pmlr-v244-teh24a,
  title = 	 {Localised Natural Causal Learning Algorithms for Weak Consistency Conditions},
  author =       {Teh, Kai and Sadeghi, Kayvan and Soo, Terry},
  booktitle = 	 {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {3345--3355},
  year = 	 {2024},
  editor = 	 {Kiyavash, Negar and Mooij, Joris M.},
  volume = 	 {244},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {15--19 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v244/main/assets/teh24a/teh24a.pdf},
  url = 	 {https://proceedings.mlr.press/v244/teh24a.html},
  abstract = 	 {By relaxing conditions for {“}natural{”} structure learning algorithms, a family of constraint-based algorithms containing all exact structure learning algorithms under the faithfulness assumption, we define localised natural structure learning algorithms (LoNS). We also provide a set of necessary and sufficient assumptions for consistency of LoNS, which can be thought of as a strict relaxation of the restricted faithfulness assumption. We provide a practical LoNS algorithm that runs in exponential time, which is then compared with related existing structure learning algorithms, namely PC/SGS and the relatively recent Sparsest Permutation algorithm. Simulation studies are also provided.}
}