Teh, KZ;
Sadeghi, K;
Soo, T;
(2024)
Localised Natural Causal Learning Algorithms for Weak Consistency Conditions.
In:
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 3345-3355).
mlr.press: Barcelona, Spain.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Localised Natural Causal Learning Algorithms for Weak Consistency Conditions |
| Event: | 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), 15-19 July 2024, Universitat Pompeu Fabra, Barcelona, Spain |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v244/teh24a.html |
| Language: | English |
| Additional information: | Published under a Creative Commons Attribution 4.0 International licence (http://creativecommons.org/licenses/by/4.0). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10202931 |
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