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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1702-1707

Higher-order cumulant-based algorithm for learning causal structure

Liao Weiguo
Zhujiang College, South China Agricultural University, Guangzhou 510900, China
DOI: 10.19734/j.issn.1001-3695.2022.10.0511

Abstract

Learning causal structure from observed data has important applications. An existing method for learning causal structure is to learn causal structure by testing the independence between noise and causal variables under the functional causal model assumption. However, such methods often involve highly computationally complex in process of testing independence, which affects the practicability and robustness of the structure learning algorithm. To this end, this paper proposed a causal structure learning algorithm that used higher-order cumulants as independence scores under a linear non-Gaussian model. The algorithm was mainly divided into two steps. The first step was to use the method based on conditional independence constraints to learn the Markov equivalence class of the causal structure. The second step was to define a score based on highorder cumulants, the score could determine the independence of two random variables so that the causal structure of the best independence score could be searched from the Markov equivalence class as the output of the algorithm. The advantages of this algorithm were: a) Compared with the independence test based on the kernel method, the method had lower computational complexity. b) The method based on score search could always obtain a model that best matches the data generation process, which improved the robustness of the learning method. Experimental results show that the high-order cumulant-based causal structure learning method improves the F1 score by 5% in synthetic data and learns more causal directions in real data.

Foundation Support

2020年广东省高等教育教学改革一般类教改项目(粤教高函【2020】20号-723)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0511
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1702-1707
Serial Number: 1001-3695(2023)06-016-1702-06

Publish History

[2023-01-13] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

廖伟国. 一种基于高阶累积量的因果结构学习算法 [J]. 计算机应用研究, 2023, 40 (6): 1702-1707. (Liao Weiguo. Higher-order cumulant-based algorithm for learning causal structure [J]. Application Research of Computers, 2023, 40 (6): 1702-1707. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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