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Frontiers of Information Technology & Electronic Engineering

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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
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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(12): 1277-1286.

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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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%V 23
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%I Zhejiang University Press & Springer

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T1 - Causality fields in nonlinear causal effect analysis
A1 - Aiguo WANG
A1 - Li LIU
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A1 - Lian LI
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VL - 23
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

非线性因果效应分析中的因果域

王爱国¹，刘礼²，杨矫云³，李廉³
¹佛山科学技术学院电子信息工程学院，中国佛山市，528225
²重庆大学大数据与软件学院，中国重庆市，400044
³合肥工业大学计算机与信息学院，中国合肥市，230009
摘要：与线性因果相比，非线性因果具有更复杂的特点和内涵。本文主要讨论非线性因果中的若干个问题，并着重强调因果域的概念。本文基于广泛应用的计算模型和方法，围绕非线性因果分析与计算以及因果域的识别问题提出相应观点和建议，并通过几个具体案例揭示非线性因果在处理复杂因果推断问题中的重要性和现实意义。

关键词组：非线性因果效应；因果域；z-特异性因果效应；正向因果；负向因果；空因果

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Reference

[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 (2^nd 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 (2^nd 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 34^th Conf on Neural Information Processing Systems, p.2734-2746.

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非线性因果效应分析中的因果域

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