基于条件独立性检验的非稳态因果发现方法

Non-Stationary Causal Discovery Method Based on Conditional Independence Test

非稳态时间序列数据之间的因果关系发现是非常重要但极具挑战的问题。现有的工作主要假设观察数据随着时间或领域发生变化。上述假设使得相关方法需要引入时间或领域作为先验知识,无法应用于分段稳态的非稳态场景。因此,提出了一种基于条件独立性检验的非稳态因果关系发现算法。首先使用变化点检测方法来识别非稳态变化的时间点,然后将上一步的时间点进行区间划分,用基于条件独立性检验的时序因果关系发现算法推断局部稳态因果结构。在仿真和真实世界数据上的实验证明了该方法的有效性。

Causal discovery on non-stationary time-series data is of great importance but challenging.Existing works mainly assume that the observed data change with time or domain,which requires the introduction of time or domain as prior knowledge.The aforementioned methods are usually unavailable on the segmented-stationary non-stationary scenarios.Therefore,this paper proposes a non-stationary causal discovery method that combines changepoint detection and structural vector auto-regressive model.It uses the changepoint detection method to identify the time point of change,then divides the time of the previous step into stationary intervals,and further uses the stationary algorithm to infer their local causal structures.Experiments on simulated and real-world data prove the effectiveness of the proposed method.