Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance

doi:10.1109/TNNLS.2022.3213641

	Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance
	Wei, Yi 1; Li, Xiaofei 1; Lin, Lihui 1; Zhu, Dengming 2; Li, Qingyong 3
	2024-04-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	35 期号:4 页码:4911-4923
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.
关键词	Asymmetry causal discovery discrete additive noise model (ANM) weighted normalized Wasserstein distance
DOI	10.1109/TNNLS.2022.3213641
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001197919500100
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39520
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.