We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control for false causal discovery, at least asymptotically. We apply our method to several real-world bivariate time series datasets and discuss its findings which mostly agree with common understanding. The arrow of time in a model can be interpreted as background knowledge on possible causal mechanisms. Hence, our ideas could be extended to incorporating different background knowledge, even for independent observations.

翻译：我们提出了一种在线性结构向量自回归模型中用于因果发现的新方法。我们将一种原本为独立观测设计的思想适应到时间序列情形，同时保留了其有利性质，即在渐近意义上对虚假因果发现进行显式误差控制。我们将该方法应用于多个真实世界的双变量时间序列数据集，并讨论了其主要与普遍认知一致的研究发现。模型中的时间箭头可被视为关于可能因果机制的先验知识。因此，我们的思想可扩展到融入不同的先验知识，即使对于独立观测也是如此。