2026-04-14T08:40:21Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1019792025-07-17T06:16:41Zcom_2072_1033col_2072_452950

Identifiability and transportability in dynamic causal networks Blondel, Gilles Arias Vicente, Marta Gavaldà Mestre, Ricard Universitat Politècnica de Catalunya. Departament de Ciències de la Computació Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica Graph theory Belief networks Dynamic causal networks Dynamic Bayesian networks Standard causal graphs Grafs, Teoria de 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 Networks, 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 identification 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 result of causal experiments in a source domain may be used for the identification of causal effects in a target domain. Preprint 2016 External research report Blondel, G., Arias, M., Gavaldà, R. "Identifiability and transportability in dynamic causal networks". 2016. https://hdl.handle.net/2117/101979 eng https://arxiv.org/abs/1610.05556 Open Access 18 p. application/pdf