  We present a constraint-based algorithm for learning causal structures from
observational time-series data, in the presence of latent confounders. We
assume a discrete-time, stationary structural vector autoregressive process,
with both temporal and contemporaneous causal relations. One may ask if
temporal and contemporaneous relations should be treated differently. The
presented algorithm gradually refines a causal graph by learning long-term
temporal relations before short-term ones, where contemporaneous relations are
learned last. This ordering of causal relations to be learnt leads to a
reduction in the required number of statistical tests. We validate this
reduction empirically and demonstrate that it leads to higher accuracy for
synthetic data and more plausible causal graphs for real-world data compared to
state-of-the-art algorithms.
