  When dealing with time series data, causal inference methods often employ
structural vector autoregressive (SVAR) processes to model time-evolving random
systems. In this work, we rephrase recursive SVAR processes with possible
latent component processes as a linear Structural Causal Model (SCM) of
stochastic processes on a simple causal graph, the \emph{process graph}, that
models every process as a single node. Using this reformulation, we generalise
Wright's well-known path-rule for linear Gaussian SCMs to the newly introduced
process SCMs and we express the auto-covariance sequence of an SVAR process by
means of a generalised trek-rule. Employing the Fourier-Transformation, we
derive compact expressions for causal effects in the frequency domain that
allow us to efficiently visualise the causal interactions in a multivariate
SVAR process. Finally, we observe that the process graph can be used to
formulate graphical criteria for identifying causal effects and to derive
algebraic relations with which these frequency domain causal effects can be
recovered from the observed spectral density.
