Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.

翻译：因果推断使我们能够发现时间序列中各种变量间的隐蔽关系。然而，现有研究中的"变量"通常指代数据维度，维度间的因果关系可能较为粗略，这阻碍了对内部关系的理解以及因果图对神经网络（NNs）的增益。本文发现，因果关系不仅存在于时间序列外部，也存在于其内部，因为时间序列反映了现实世界中的连续事件——这启发我们探索内部子序列间的关联。然而，挑战在于：如何从子序列中挖掘因果关系，以及如何利用因果自然结构改进神经网络。为应对这些挑战，我们提出一种名为"挖掘因果自然结构"（MCNS）的新颖框架，该框架具有自动化和领域无关特性，通过内部因果机制帮助发现时间序列中的因果自然结构。我们在时间序列分类任务上评估了MCNS框架及其对神经网络的注入效果。实验结果表明，通过精炼注意力机制、形态选择分类与数据集剪枝，MCNS驱动神经网络（甚至数据本身）获得更优的准确率与可解释性。此外，MCNS还为时间序列及数据集提供了深入且坚实的总结。