
Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI. Causality fields in nonlinear causal effect analysis[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200165 @article{title="Causality fields in nonlinear causal effect analysis", %0 Journal Article TY - JOUR
非线性因果效应分析中的因果域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: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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 (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. CLC number: On-line Access: 2024-08-27 Received: 2023-10-17 Revision Accepted: 2024-05-08 Crosschecked: 2022-08-29 Cited: 0 Clicked: 3728 Citations: Bibtex RefMan EndNote GB/T7714 Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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