  This study addresses the problem of learning an extended summary causal graph
on time series. The algorithms we propose fit within the well-known
constraint-based framework for causal discovery and make use of
information-theoretic measures to determine (in)dependencies between time
series. We first introduce generalizations of the causation entropy measure to
any lagged or instantaneous relations, prior to using this measure to construct
extended summary causal graphs by adapting two well-known algorithms, namely PC
and FCI. The behavior of our methods is illustrated through several experiments
run on simulated and real datasets.
