  We consider a bivariate time series $(X_t,Y_t)$ that is given by a simple
linear autoregressive model. Assuming that the equations describing each
variable as a linear combination of past values are considered structural
equations, there is a clear meaning of how intervening on one particular $X_t$
influences $Y_{t'}$ at later times $t'>t$. In the present work, we describe
conditions under which one can define a causal model between variables that are
coarse-grained in time, thus admitting statements like `setting $X$ to $x$
changes $Y$ in a certain way' without referring to specific time instances. We
show that particularly simple statements follow in the frequency domain, thus
providing meaning to interventions on frequencies.
