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Localised Natural Causal Learning Algorithms for Weak Consistency Conditions

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. Green open access

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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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