提出可扩展的动态因果发现方法，捕捉随时间变化的因果关系。
UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
- 用解耦与重构网络分离时序数据语义，通过自回归依赖矩阵建模变量关系。
- 通过时间扰动下的预测误差分析，精准估计动态因果影响。
- 在合成与真实数据（如人体运动）中均能有效捕捉演化中的因果结构。
从观测时序数据中揭示因果关系是理解复杂系统的基础。尽管许多方法仅能推断静态因果图，但现实世界系统常表现出动态因果性——即关系随时间演变。准确捕捉这些时序动态需要时变因果图。本文提出UnCLe，一种新型深度学习方法，用于可扩展的动态因果发现。UnCLe采用一对解耦器（Uncoupler）和重构器（Recoupler）网络，将输入时序数据分解为语义表示，并通过自回归依赖矩阵学习变量间依赖关系。它通过分析由时间扰动引发的逐点预测误差来估计动态因果影响。大量实验表明，UnCLe不仅在静态因果发现基准上优于现有最先进方法，更重要的是，能够准确捕捉并表征合成与真实动态系统（如人体运动）中的时变因果关系。UnCLe为揭示复杂现象背后的时间演化机制提供了有前景的解决方案。
原文摘要 · Abstract (English)
Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit dynamic causality-where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery. UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations and learns inter-variable dependencies via auto-regressive Dependency Matrices. It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations. Extensive experiments demonstrate that UnCLe not only outperforms state-of-the-art baselines on static causal discovery benchmarks but, more importantly, exhibits a unique capability to accurately capture and represent evolving temporal causality in both synthetic and real-world dynamic systems (e.g., human motion). UnCLe offers a promising approach for revealing the underlying, time-varying mechanisms of complex phenomena.
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