基于线性反演模型的梁-克莱曼因果关系分析

Justin Lien
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引用次数: 0

摘要

因果分析是通过量化一个变量对另一个变量的影响来确定系统中变量之间因果关系的有力工具。尽管该领域取得了重大进展,但现有的许多研究都局限于单向因果关系或高斯外力作用,从而限制了它们对复杂现实问题的适用性。本研究针对复杂随机微分系统的因果关系分析,提出了一种新颖的数据驱动方法,整合了梁-克莱曼信息流和线性逆建模的概念。我们的方法将环境噪声建模为无记忆高斯白噪声或有记忆奥恩斯坦-乌伦贝克彩色噪声,并允许自因和互因,从而提供了对底层系统更真实的表示和解释。此外,这种基于 LIM 的方法可以识别动态和相关性对因果关系的单独贡献。一般来说,无论使用哪种噪声,厄尔尼诺/南方涛动和印度洋偶极子之间的因果关系都是相互的,但并不对称,因果关系图反映了类似厄尔尼诺/南方涛动的模式,这与之前的研究一致。值得注意的是,在彩色噪声的情况下,噪声记忆图显示了 Ni\~no 3 区域的热点,这与信息流进一步相关。这表明我们的方法提供了一个更全面的框架,为全球气候系统的因果推断提供了更深刻的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Liang-Kleeman Causality Analysis based on Linear Inverse Modeling
Causality analysis is a powerful tool for determining cause-and-effect relationships between variables in a system by quantifying the influence of one variable on another. Despite significant advancements in the field, many existing studies are constrained by their focus on unidirectional causality or Gaussian external forcing, limiting their applicability to complex real-world problems. This study proposes a novel data-driven approach to causality analysis for complex stochastic differential systems, integrating the concepts of Liang-Kleeman information flow and linear inverse modeling. Our method models environmental noise as either memoryless Gaussian white noise or memory-retaining Ornstein-Uhlenbeck colored noise, and allows for self and mutual causality, providing a more realistic representation and interpretation of the underlying system. Moreover, this LIM-based approach can identify the individual contribution of dynamics and correlation to causality. We apply this approach to re-examine the causal relationships between the El Ni\~{n}o-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), two major climate phenomena that significantly influence global climate patterns. In general, regardless of the type of noise used, the causality between ENSO and IOD is mutual but asymmetric, with the causality map reflecting an ENSO-like pattern consistent with previous studies. Notably, in the case of colored noise, the noise memory map reveals a hotspot in the Ni\~no 3 region, which is further related to the information flow. This suggests that our approach offers a more comprehensive framework and provides deeper insights into the causal inference of global climate systems.
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