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On-line Access: 2022-08-22
Received: 2022-04-21
Revision Accepted: 2022-05-05
Crosschecked: 2022-08-29
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Citations: Bibtex RefMan EndNote GB/T7714
Causality fields in nonlinear causal effect analysis
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Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI. Causality fields in nonlinear causal effect analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1277-1286.
@article{title="Causality fields in nonlinear causal effect analysis",
author="Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="8",
pages="1277-1286",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200165"
}
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%A Aiguo WANG
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%V 23
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200165
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T1 - Causality fields in nonlinear causal effect analysis
A1 - Aiguo WANG
A1 - Li LIU
A1 - Jiaoyun YANG
A1 - Lian LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200165
Abstract: Compared with linear causality, nonlinear causality has more complex characteristics and content. In this paper, we discuss certain issues related to nonlinear causality with an emphasis on the concept of causality field. Based on widely used computation models and methods, we present some viewpoints and opinions on the analysis and computation of nonlinear causality and the identification problem of causality fields. We also reveal the importance and practical significance of nonlinear causality in handling complex causal inference problems via several specific examples.
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