Teh, Kai Z;
Sadeghi, kayvan;
Soo, Terry;
(2024)
Localised Natural Causal Learning Algorithms for Weak Consistency Conditions.
In:
Proceedings of Machine Learning Research.
: Barcelona, Spain.
(In press).
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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 |
| Location: | Universitat Pompeu Fabra, Barcelona, Spain |
| Dates: | 15 Jul 2024 - 19 Jul 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/ |
| Language: | English |
| 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/10192525 |
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