  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.
