Identifiability and Transportability in Dynamic Causal Networks
  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. 
Gilles Blondel and Marta Arias and Ricard Gavald\`a
cs.AI