融合先验约束的拓扑霍克斯过程格兰杰因果发现算法

离散时序数据的格兰杰因果关系发现算法具有重要应用价值。现有方法主要采用霍克斯过程建模，无法适用于非独立同分布数据和带有时间误差的数据。为此，提出了一种融合先验约束的拓扑霍克斯过程格兰杰因果关系发现算法（PTHP）。首先，使用基于约束的方法筛选出一批显著性水平较高的因果边，提升算法对故障发生时间误差的容忍性；随后，将上一步获取的边作为先验约束融合到拓扑霍克斯过程中，解决序列间的非独立同分布问题。模拟数据和真实数据的实验证明了该方法的有效性，并获得了PCIC 2021因果推理大赛第一名。

Granger causality discovery algorithm for topological Hawkes processes with priori-constraints

Granger causality discovery algorithm for discrete-time series data has important application value. The existing methods mainly use Hawkes processes modeling, which can not be applied to non-iid data and data with time-skew errors. Therefore, this paper proposed a Granger causality discovery algorithm(PTHP) for topological Hawkes processes integrating a priori constraints. Firstly, it used the constraint-based method to screen a group of causal edges with a high significance level to improve the tolerance of the algorithm to the fault time-skew errors. Then, the edges obtained in the previous step were fused into the topological Hawkes processes as a priori constraints to solve the non-iid problem between sequences. Experiments on simulated data and real-world data show the effectiveness of this method, and it won first place in PCIC 2021 causal inference competition.