Identifying causality is a challenging task in many data-intensive scenarios.
Many algorithms have been proposed for this critical task. However, most of
them consider the learning algorithms for directed acyclic graph (DAG) of
Bayesian network (BN). These BN-based models only have limited causal
explainability because of the issue of Markov equivalence class. Moreover, they
are dependent on the assumption of stationarity, whereas many sampling time
series from complex system are nonstationary. The nonstationary time series
bring dataset shift problem, which leads to the unsatisfactory performances of
these algorithms. To fill these gaps, a novel causation model named Unique
Causal Network (UCN) is proposed in this paper. Different from the previous
BN-based models, UCN considers the influence of time delay, and proves the
uniqueness of obtained network structure, which addresses the issue of Markov
equivalence class. Furthermore, based on the decomposability property of UCN, a
higher-order causal entropy (HCE) algorithm is designed to identify the
structure of UCN in a distributed way. HCE algorithm measures the strength of
causality by using nearest-neighbors entropy estimator, which works well on
nonstationary time series. Finally, lots of experiments validate that HCE
algorithm achieves state-of-the-art accuracy when time series are
nonstationary, compared to the other baseline algorithms.

本文提出了一种新的因果模型 Unique Causal Network，解决了基于贝叶斯网络的模型在因果解释性上存在的问题，同时提出了基于高阶因果熵算法的分布式 UCN 结构识别方法，用于处理非平稳时间序列数据，实验证明该方法在非平稳时间序列数据上具有最先进的准确性。