论文标题
使用PC算法的时间序列及其时间感知扩展的一致因果推断
Consistent Causal Inference from Time Series with PC Algorithm and its Time-Aware Extension
论文作者
论文摘要
众所周知，具有PC算法的因果定向无环图（DAG）的估计量是基于独立且相同分布的样品一致的。在本文中，我们考虑了多元样本相同分布但不是独立的情况。一个常见的例子是固定的多元时间序列。我们表明，根据涉及$ρ$混合的基础时间序列的一组标准假设，PC算法在此相关样本方案中是一致的。此外，我们表明，对于流行的时间序列模型，例如矢量自动回归运动平均值和线性过程，PC算法的一致性保持。我们还证明了时间感知的PC算法的一致性，PC算法最近改编了时间序列方案。我们的发现得到了仿真和基准在本文末尾提供的实际数据分析的支持。
The estimator of a causal directed acyclic graph (DAG) with the PC algorithm is known to be consistent based on independent and identically distributed samples. In this paper, we consider the scenario when the multivariate samples are identically distributed but not independent. A common example is a stationary multivariate time series. We show that under a standard set of assumptions on the underlying time series involving $ρ$-mixing, the PC algorithm is consistent in this dependent sample scenario. Further, we show that for the popular time series models such as vector auto-regressive moving average and linear processes, consistency of the PC algorithm holds. We also prove the consistency for the Time-Aware PC algorithm, a recent adaptation of the PC algorithm for the time series scenario. Our findings are supported by simulations and benchmark real data analyses provided towards the end of the paper.
