Localised Natural Causal Learning Algorithms for Weak Consistency Conditions

Kai Teh, Kayvan Sadeghi, Terry Soo
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:3345-3355, 2024.

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 this Paper

BibTeX
@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.} }
Endnote
%0 Conference Paper %T Localised Natural Causal Learning Algorithms for Weak Consistency Conditions %A Kai Teh %A Kayvan Sadeghi %A Terry Soo %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-teh24a %I PMLR %P 3345--3355 %U https://proceedings.mlr.press/v244/teh24a.html %V 244 %X 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.
APA
Teh, K., Sadeghi, K. & Soo, T.. (2024). Localised Natural Causal Learning Algorithms for Weak Consistency Conditions. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:3345-3355 Available from https://proceedings.mlr.press/v244/teh24a.html.

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