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Soft Computing

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03.08.2016 | Methodologies and Application

An efficient algorithm for large-scale causal discovery

verfasst von: Yinghan Hong, Zhusong Liu, Guizhen Mai

Erschienen in: Soft Computing | Ausgabe 24/2017

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Abstract

Causal discovery is a fundamental problem in scientific research. Although many researchers are committed to finding causal relationships from observational data, large-scale causal discovery remains a tremendous challenge. In this paper, a new approach for large-scale causal discovery is proposed, based on a split-and-merge strategy. The method first splits a given dataset into small subdatasets using a graph-partitioning method and then develops a effective algorithm to infer the causality of each subdataset. The entire causal structure with respect to the given dataset is achieved by combining all the causalities of each subdataset. The experimental results show that the proposed approach is effective and scalable for large-scale causal discovery problems.

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Titel: An efficient algorithm for large-scale causal discovery
verfasst von: Yinghan Hong
Zhusong Liu
Guizhen Mai
Publikationsdatum: 03.08.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 24/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2281-0

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