Abstract:

Time-series data exhibits recency characteristic， i.e.， variable values are generally dependent on recent historical information. Existing time-series causal inference methods do not fully consider the recency characteristic， which use a uniform threshold when inferring causal relationships with different delays through hypothesis testing， so that it is difficult to effectively infer weaker causal relationships. To address the aforementioned issue， a method for time-series causal inference based on adaptive threshold learning was proposed. Firstly， data characteristics were extracted. Then， based on the data characteristics at different delays， a combination of thresholds used in the hypothesis testing process was automatically learned. Finally， this threshold combination was applied to the hypothesis testing processes of the PC （Peter-Clark） algorithm， PCMCI （Peter-Clark and Momentary Conditional Independence） algorithm， and VAR-LINGAM （Vector AutoRegressive LINear non-Gaussian Acyclic Model） algorithm to obtain more accurate causal relationship structures. Experimental results on the simulation dataset show that the F1 values of adaptive PC algorithm， adaptive PCMCI algorithm， and adaptive VAR-LINGAM algorithm using the proposed method are all improved.

Key words: causal inference, time series, hypothesis testing, parameter optimization, adaptive

摘要：

时序数据存在近因性特点，即变量值普遍依赖近期的历史信息，而现有时序因果推断方法没有充分考虑时序数据的这种特性，在通过假设检验推断不同延迟的因果关系时使用统一的阈值，难以有效推断较弱的因果关系。针对上述问题，提出基于自适应阈值学习的时序因果推断方法：首先提取数据特性，其次根据不同延迟下数据呈现的性质，自动地学习假设检验过程中使用的阈值组合，最后将该阈值组合用于PC（Peter-Clark）算法、PCMCI（Peter-Clark and Momentary Conditional Independence）算法和VAR-LINGAM（Vector AutoRegressive LINear non-Gaussian Acyclic Model）算法的假设检验过程，以得到更准确的因果关系结构。在仿真数据集上的实验结果表明，采用所提方法的自适应PC算法、自适应PCMCI算法和自适应VAR-LINGAM算法的F1值都有所提高。

关键词: 因果推断, 时间序列, 假设检验, 参数优化, 自适应