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Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance

文献类型：期刊论文

作者Wei, Yi1; Li, Xiaofei1; Lin, Lihui1; Zhu, Dengming2; Li, Qingyong3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2024-04-01
卷号35期号:4页码:4911-4923
关键词Asymmetry causal discovery discrete additive noise model (ANM) weighted normalized Wasserstein distance
ISSN号2162-237X
DOI10.1109/TNNLS.2022.3213641
英文摘要The task of causal discovery from observational data (X,Y) is defined as the task of deciding whether X causes Y , or Y causes X or if there is no causal relationship between X and Y . Causal discovery from observational data is an important problem in many areas of science. In this study, we propose a method to address this problem when the cause-and-effect relationship is represented by a discrete additive noise model (ANM). First, assuming that X causes Y , we estimate the conditional distributions of the noise given X using regression. Similarly, assuming that Y causes X , we also estimate the conditional distributions of noise given Y . Based on the structural characteristics of the discrete ANM, we find that the dissimilarity of the conditional distributions of noise in the causal direction is smaller than that in the anticausal direction. Then, we propose a weighted normalized Wasserstein distance to measure the dissimilarity of the conditional distributions of noise. Finally, we propose a decision rule for casual discovery by comparing two computed weighted normalized Wasserstein distances. An empirical investigation demonstrates that our method performs well on synthetic data and outperforms state-of-the-art methods on real data.
资助项目National Natural Science Foundation of China[62276019] ; National Natural Science Foundation of China[U2034211] ; NIM Research and Development Project[35-AKYZD2116-1] ; Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences[YJKYYQ20190055] ; Natural Science Foundation of Fujian Province[2021J011143] ; Natural Science Foundation of Fujian Province[2020J01421] ; Research and Development Project of Wuyi University[2018J01562-01]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001197919500100
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39519]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wei, Yi
作者单位1.Wuyi Univ, Sch Math & Comp Sci, Fujian Key Lab Big Data Applicat & Intellectualiz, Nanping 354300, Fujian, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Beijing Jiaotong Univ, Frontiers Sci Ctr Smart High Speed Railway Syst, Beijing 100044, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Yi,Li, Xiaofei,Lin, Lihui,et al. Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2024,35(4):4911-4923.
APA Wei, Yi,Li, Xiaofei,Lin, Lihui,Zhu, Dengming,&Li, Qingyong.(2024).Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,35(4),4911-4923.
MLA Wei, Yi,et al."Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35.4(2024):4911-4923.

入库方式： OAI收割

来源：计算技术研究所

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