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Title: Scalable Causal Graph Learning through a Deep Neural Network

Abstract

Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.

Authors:
;
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
OSTI Identifier:
1566865
Report Number(s):
BNL-212124-2019-COPA
DOE Contract Number:  
SC0012704
Resource Type:
Conference
Resource Relation:
Conference: CIKM 2019, Beijing China, 11/3/2019 - 11/7/2019
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Xu, Chenxiao, and Yoo, Shinaje. Scalable Causal Graph Learning through a Deep Neural Network. United States: N. p., 2019. Web.
Xu, Chenxiao, & Yoo, Shinaje. Scalable Causal Graph Learning through a Deep Neural Network. United States.
Xu, Chenxiao, and Yoo, Shinaje. Sun . "Scalable Causal Graph Learning through a Deep Neural Network". United States. https://www.osti.gov/servlets/purl/1566865.
@article{osti_1566865,
title = {Scalable Causal Graph Learning through a Deep Neural Network},
author = {Xu, Chenxiao and Yoo, Shinaje},
abstractNote = {Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Conference:
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