Nonlinear Causal Discovery for High-Dimensional Deterministic Data

doi:10.1109/TNNLS.2021.3106111

	Nonlinear Causal Discovery for High-Dimensional Deterministic Data
	Zeng, Yan 1,2; Hao, Zhifeng 3; Cai, Ruichu 1,4; Xie, Feng 5; Huang, Libo 6; Shimizu, Shohei 7,8
	2021-09-02
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	12
摘要	Nonlinear causal discovery with high-dimensional data where each variable is multidimensional plays a significant role in many scientific disciplines, such as social network analysis. Previous work majorly focuses on exploiting asymmetry in the causal and anticausal directions between two high-dimensional variables (a cause-effect pair). Although there exist some works that concentrate on the causal order identification between multiple variables, i.e., more than two high-dimensional variables, they do not validate the consistency of methods through theoretical analysis on multiple-variable data. In particular, based on the asymmetry for the cause-effect pair, if model assumptions for any pair of the data are violated, the asymmetry condition will not hold, resulting in the deduction of incorrect order identification. Thus, in this article, we propose a causal functional model, namely high-dimensional deterministic model (HDDM), to identify the causal orderings among multiple high-dimensional variables. We derive two candidates' selection rules to alleviate the inconvenient effects resulted from the violated-assumption pairs. The corresponding theoretical justification is provided as well. With these theoretical results, we develop a method to infer causal orderings for nonlinear multiple-variable data. Simulations on synthetic data and real-world data are conducted to verify the efficacy of our proposed method. Since we focus on deterministic relations in our method, we also verify the robustness of the noises in simulations.
关键词	Integrated circuit modeling Data models Linearity Kernel Learning systems Hilbert space Covariance matrices Causal ordering deterministic relations high-dimensional data nonlinear causal discovery
DOI	10.1109/TNNLS.2021.3106111
收录类别	SCI
语种	英语
资助项目	NSFC-Guangdong Joint Fund[U1501254] ; Natural Science Foundation of China[61876043] ; Natural Science Foundation of China[61472089] ; Natural Science Foundation of Guangdong[2014A030306004] ; Natural Science Foundation of Guangdong[2014A030308008] ; Science and Technology Planning Project of Guangdong[201902010058] ; China Scholarship Council (CSC) ; ONR[N00014-20-1-2501] ; KAKENHI[20K11708]
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:000732084100001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17940
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Hao, Zhifeng; Cai, Ruichu
作者单位	1.Guangdong Univ Technol, Sch Comp, Guangzhou 510006, Peoples R China 2.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 3.Shantou Univ, Coll Sci, Shantou 515063, Guangdong, Peoples R China 4.Pazhou Lab, Guangzhou 510006, Peoples R China 5.Peking Univ, Sch Math Sci, Beijing 100084, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing 100084, Peoples R China 7.Shiga Univ, Fac Data Sci, Hikone 5228522, Japan 8.RIKEN, Ctr Adv Intelligence Project AIP, Tokyo 1030027, Japan
推荐引用方式 GB/T 7714	Zeng, Yan,Hao, Zhifeng,Cai, Ruichu,et al. Nonlinear Causal Discovery for High-Dimensional Deterministic Data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12.
APA	Zeng, Yan,Hao, Zhifeng,Cai, Ruichu,Xie, Feng,Huang, Libo,&Shimizu, Shohei.(2021).Nonlinear Causal Discovery for High-Dimensional Deterministic Data.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Zeng, Yan,et al."Nonlinear Causal Discovery for High-Dimensional Deterministic Data".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):12.