  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.
