  In this paper we propose a causal analog to the purely observational Dynamic
Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound
and complete algorithm for identification of Dynamic Causal Net- works, namely,
for computing the effect of an intervention or experiment, based on passive
observations only, whenever possible. We note the existence of two types of
confounder variables that affect in substantially different ways the iden-
tification procedures, a distinction with no analog in either Dynamic Bayesian
Networks or standard causal graphs. We further propose a procedure for the
transportability of causal effects in Dynamic Causal Network settings, where
the re- sult of causal experiments in a source domain may be used for the
identification of causal effects in a target domain.
