  A structural vector autoregressive (SVAR) process is a linear causal model
for variables that evolve over a discrete set of time points and between which
there may be lagged and instantaneous effects. The qualitative causal structure
of an SVAR process can be represented by its finite and directed process graph,
in which a directed link connects two processes whenever there is a lagged or
instantaneous effect between them. At the process graph level, the causal
structure of SVAR processes is compactly parameterised in the frequency domain.
In this paper, we consider the problem of causal discovery and causal effect
estimation from the spectral density, the frequency domain analogue of the auto
covariance, of the SVAR process. Causal discovery concerns the recovery of the
process graph and causal effect estimation concerns the identification and
estimation of causal effects in the frequency domain.
  We show that information about the process graph, in terms of $d$- and
$t$-separation statements, can be identified by verifying algebraic constraints
on the spectral density. Furthermore, we introduce a notion of rational
identifiability for frequency causal effects that may be confounded by
exogenous latent processes, and show that the recent graphical latent factor
half-trek criterion can be used on the process graph to assess whether a given
(confounded) effect can be identified by rational operations on the entries of
the spectral density.
