MrPC: causal structure learning in distributed systems

Nguyen, Thin, Nguyen, Duc Thanh, Le, Thuc Duy and Venkatesh, Svetha 2020, MrPC: causal structure learning in distributed systems, in ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020, Springer, Cham, Switzerland, pp. 87-94, doi: 10.1007/978-3-030-63820-7_10.

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Title MrPC: causal structure learning in distributed systems
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Nguyen, Duc ThanhORCID iD for Nguyen, Duc Thanh orcid.org/0000-0002-2285-2066
Le, Thuc Duy
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Neural Information Processing. International Conference (27th : 2020 : Online from Bangkok, Thailand)
Conference location Online from Bangkok, Thailand
Conference dates 2020/11/18 - 2020/11/22
Title of proceedings ICONIP 2020 : Proceedings of the 27th International Conference on Neural Information Processing 2020
Editor(s) Yang, H
Pasupa, K
Leung, AC-S
Kwok, JT
Chan, JH
King, I
Publication date 2020
Series Neural Information Processing International Conference
Start page 87
End page 94
Total pages 8
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) Causality
Explainable AI
Causal structure learning
Distributed systems
Summary PC algorithm (PC) – named after its authors, Peter and Clark – is an advanced constraint based method for learning causal structures. However, it is a time-consuming algorithm since the number of independence tests is exponential to the number of considered variables. Attempts to parallelise PC have been studied intensively, for example, by distributing the tests to all computing cores in a single computer. However, no effort has been made to speed up PC through parallelising the conditional independence tests into a cluster of computers. In this work, we propose MrPC, a robust and efficient PC algorithm, to accelerate PC to serve causal discovery in distributed systems. Alongside with MrPC, we also propose a novel manner to model non-linear causal relationships in gene regulatory data using kernel functions. We evaluate our method and its variants in the task of building gene regulatory networks. Experimental results on benchmark datasets show that the proposed MrPCgains up to seven times faster than sequential PC implementation. In addition, kernel functions outperform conventional linear causal modelling approach across different datasets.
ISBN 9783030638191
ISSN 1865-0929
1865-0937
Language eng
DOI 10.1007/978-3-030-63820-7_10
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146557

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