HANM: Hierarchical Additive Noise Model for Many-to-One Causality Discovery-Reference-Cited by-同舟云学术

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HANM: Hierarchical Additive Noise Model for Many-to-One Causality Discovery

Published:2023-12-01 Issue:12 Volume:35 Page:12708-12720
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhao Boxiang¹^ORCID,Wang Shuliang¹^ORCID,Chi Lianhua²^ORCID,Zhao Chuanfeng³^ORCID,Yuan Hanning¹^ORCID,Li Qi¹^ORCID,Liu Xiaojia¹^ORCID,Geng Jing¹^ORCID,Yuan Ye¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

2. School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC, Australia

3. Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10129050.pdf?arnumber=10129050

Reference44 articles.

1. Information-geometric approach to inferring causal directions

2. The Blessings of Multiple Causes

3. Inferring deterministic causal relations;daniusis;Proc 26th Conf Uncertainty Artif Intell,2010

4. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model;shimizu;J Mach Learn Res,2011

5. Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection

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