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Nonlinear Causal Discovery for High-Dimensional Deterministic Data

文献类型：期刊论文

作者Zeng, Yan7,8; Hao, Zhifeng6; Cai, Ruichu5,8; Xie, Feng4; Huang, Libo3; Shimizu, Shohei1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-09-02
页码12
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
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3106111
英文摘要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.
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
语种英语
WOS记录号WOS:000732084100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17939]  
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.RIKEN, Ctr Adv Intelligence Project AIP, Tokyo 1030027, Japan
2.Shiga Univ, Fac Data Sci, Hikone 5228522, Japan
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100084, Peoples R China
4.Peking Univ, Sch Math Sci, Beijing 100084, Peoples R China
5.Pazhou Lab, Guangzhou 510006, Peoples R China
6.Shantou Univ, Coll Sci, Shantou 515063, Guangdong, Peoples R China
7.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
8.Guangdong Univ Technol, Sch Comp, Guangzhou 510006, Peoples R China
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.

入库方式： OAI收割

来源：计算技术研究所

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