Accelerating linear causal model discovery using hoeffding bounds

Li, Gang, Dai, Honghua, Tu, Yiqing and Kurt, Tarkan 2004, Accelerating linear causal model discovery using hoeffding bounds, Lecture notes in computer science, vol. 3157, pp. 201-210.

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Title Accelerating linear causal model discovery using hoeffding bounds
Author(s) Li, GangORCID iD for Li, Gang
Dai, HonghuaORCID iD for Dai, Honghua
Tu, Yiqing
Kurt, Tarkan
Journal name Lecture notes in computer science
Volume number 3157
Start page 201
End page 210
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2004-09-21
ISSN 0302-9743
Summary Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithms proposed by Dai et al. has demonstrated the ability of the Minimum Message Length (MML) principle in discovering Linear Causal Models from training data. In order to further explore ways to improve efficiency, this paper incorporates the Hoeffding Bounds into the learning process. At each step of causal discovery, if a small number of data items is enough to distinguish the better model from the rest, the computation cost will be reduced by ignoring the other data items. Experiments with data set from related benchmark models indicate that the new algorithm achieves speedup over previous work in terms of learning efficiency while preserving the discovery accuracy.
Language eng
Field of Research 080309 Software Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag
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Document type: Journal Article
Collection: School of Information Technology
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