从数据中挖掘随机非线性系统的因果关系，提升预测与决策能力
Building causation links in stochastic nonlinear systems from data
- 基于物理响应理论框架，结合先进机器学习方法建模
- 在大规模马尔可夫网络中实现线性响应因果预测的渐近效率分析
- 适用于复杂系统因果推断，尤其适合科研与工程决策场景
因果关系在理解世界中起着基础作用。识别和理解因果关系对于做出明智决策、预测结果和制定有效策略至关重要。然而，仅凭观测数据难以确定因果关系，因为相关性未必意味着因果性。近年来，机器学习成为揭示隐藏因果机制、理解复杂系统的新工具。本文在物理学响应理论框架下，研究一大类复杂系统的内在因果链接检测问题。我们发展了[1]提出的理论思想，并采用前沿机器学习技术从数据构建模型。研究涵盖线性和非线性随机系统，并在大规模线性相互作用马尔可夫过程网络中计算了基于线性响应的因果预测器的渐近效率。
原文摘要 · Abstract (English)
Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. In this work, we address the issue of detecting the intrinsic causal links of a large class of complex systems in the framework of the response theory in physics. We develop some theoretical ideas put forward by [1], and technically we use state-of-the-art ML techniques to build up models from data. We consider both linear stochastic and non-linear systems. Finally, we compute the asymptotic efficiency of the linear response based causal predictor in a case of large scale Markov process network of linear interactions.
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