通过逆向追踪观测效应，实时捕捉复杂系统中动态变化的因果关系。
Assimilative Causal Inference
- 基于贝叶斯数据同化，从结果反推原因，解决因果逆问题。
- 可在短数据集上识别动态因果关系，支持高维系统建模。
- 适合研究具有间歇性与极端事件的复杂系统，如气候或神经网络。
因果推断在各科学领域至关重要，但现有方法难以捕捉复杂高维系统中瞬时且随时间演变的因果关系。本文提出融合因果推断（ACI）方法框架，利用贝叶斯数据同化技术，从观测效应逆向追溯原因。与传统前向影响量化不同，ACI解决的是逆问题，无需观测候选原因即可识别动态因果交互，可处理短数据集，并可通过高效数据同化算法扩展至高维场景。关键优势在于实现因果角色的在线追踪，即使其周期性反转亦可捕捉；并提供数学严谨的因果影响范围判据，揭示效应传播距离。实验验证了其在展现间歇性和极端事件的复杂动力系统中的有效性。该方法为研究瞬态因果结构至关重要的复杂系统开辟了新路径。
原文摘要 · Abstract (English)
Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward from observed effects. ACI solves the inverse problem rather than quantifying forward influence. It uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and, in principle, can be implemented in high-dimensional settings by employing efficient data assimilation algorithms. Crucially, it provides online tracking of causal roles that may reverse intermittently and facilitates a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events. ACI opens valuable pathways for studying complex systems, where transient causal structures are critical.
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