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.
翻译:在许多数据密集型情景中,确定因果关系是一项艰巨的任务。 许多算法都为这一关键任务提出了许多挑战性任务。 但是,大多数算法都考虑到了巴伊西亚网络(BN)定向循环图(DAG)的学习算法(DAG),这些以BN为基础的模型仅仅由于Markov等效等级问题而具有有限的因果关系解释。此外,这些模型取决于对静止性的假设,而复杂的系统的许多抽样时间序列是非静止的。非静止时间序列带来了数据元件转换问题,导致这些算法的性能不令人满意。为了填补这些空白,本文件提出了名为Unique Causal 网络(UCN)的新型因果关系模型。与以前以BN为基础的模型不同的是,BNAGN的模型只考虑时间延迟的影响,并证明获得的网络结构的独特性,它解决了Markov等值等级的问题。此外,根据UCN的不兼容性属性,一个更高等级的因子因子酶(HCE)算法旨在以分布的方式确定UCN的结构。HCE算算算算方法,用最近的因果关系模型测算出不因力序列,在最接近的模型中,在最后的模型中,最终的轨轨数级的序列中可以实现其他的轨数级的序列。