Full Text:   <2314>

Summary:  <294>

CLC number: 

On-line Access: 2022-08-22

Received: 2022-04-21

Revision Accepted: 2022-05-05

Crosschecked: 2022-08-29

Cited: 0

Clicked: 1746

Citations:  Bibtex RefMan EndNote GB/T7714


Aiguo WANG




-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.8 P.1277-1286


Causality fields in nonlinear causal effect analysis

Author(s):  Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI

Affiliation(s):  School of Electronic Information Engineering, Foshan University, Foshan 528225, China; more

Corresponding email(s):   dcsliuli@cqu.edu.cn, jiaoyun@hfut.edu.cn, wangaiguo2546@163.com, llian@hfut.edu.cn

Key Words: 

Share this article to: More <<< Previous Article|

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",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Causality fields in nonlinear causal effect analysis
%A Aiguo WANG
%A Jiaoyun YANG
%A Lian LI
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 8
%P 1277-1286
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200165

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
IS - 8
SP - 1277
EP - 1286
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200165

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Guo RC, Cheng L, Li JD, et al., 2021. A survey of learning causality with data: problems and methods. ACM Comput Surv, 53(4):75.

[2]Pearl J, 2009. Causality: Models, Reasoning, and Inference (2nd Ed.). Cambridge University Press, Cambridge, UK.

[3]Pearl J, 2019. The seven tools of causal inference, with reflections on machine learning. Commun ACM, 62(3):54-60.

[4]Rubin DB, 2005. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc, 100(469):322-331.

[5]Schölkopf B, Locatello F, Bauer S, et al., 2021. Toward causal representation learning. Proc IEEE, 109(5):612-634.

[6]Spirtes P, Zhang K, 2016. Causal discovery and inference: concepts and recent methodological advances. Appl Inform, 3:3.

[7]Stavroglou SK, Pantelous AA, Stanley HE, et al., 2019. Hidden interactions in financial markets. Proc Nat Acad Sci USA, 116(22):10646-10651.

[8]Sugihara G, May R, Ye H, et al., 2012. Detecting causality in complex ecosystems. Science, 338(6106):496-500.

[9]Takeuchi Y, Du NH, Hieu NT, et al., 2006. Evolution of predator–prey systems described by a Lotka–Volterra equation under random environment. J Math Anal Appl, 323(2):938-957.

[10]von Kügelgen J, Gresele L, Schölkopf B, 2021. Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects. IEEE Trans Artif Intell, 2(1):18-27.

[11]Wooldridge JM, 2010. Econometric Analysis of Cross Section and Panel Data (2nd Ed.). MIT Press, Cambridge, USA.

[12]Yao LY, Chu ZX, Li S, et al., 2021. A survey on causal inference. ACM Trans Knowl Discov Data, 15(5):74.

[13]Yue ZQ, Zhang HW, Sun QR, et al., 2020. Interventional few-shot learning. Proc 34th Conf on Neural Information Processing Systems, p.2734-2746.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE