Localised Natural Causal Learning Algorithms for Weak Consistency Conditions
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.
Cite
Text
Teh et al. "Localised Natural Causal Learning Algorithms for Weak Consistency Conditions." Uncertainty in Artificial Intelligence, 2024.Markdown
[Teh et al. "Localised Natural Causal Learning Algorithms for Weak Consistency Conditions." Uncertainty in Artificial Intelligence, 2024.](https://mlanthology.org/uai/2024/teh2024uai-localised/)BibTeX
@inproceedings{teh2024uai-localised,
title = {{Localised Natural Causal Learning Algorithms for Weak Consistency Conditions}},
author = {Teh, Kai and Sadeghi, Kayvan and Soo, Terry},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2024},
pages = {3345-3355},
volume = {244},
url = {https://mlanthology.org/uai/2024/teh2024uai-localised/}
}