  We propose an approach for learning the causal structure in stochastic
dynamical systems with a $1$-step functional dependency in the presence of
latent variables. We propose an information-theoretic approach that allows us
to recover the causal relations among the observed variables as long as the
latent variables evolve without exogenous noise. We further propose an
efficient learning method based on linear regression for the special sub-case
when the dynamics are restricted to be linear. We validate the performance of
our approach via numerical simulations.
