The causal manipulation of chain event graphs
| dc.creator | Riccomagno, Eva | |
| dc.creator | Smith, Jim Q. | |
| dc.date | 2007-09-21 | |
| dc.date.accessioned | 2026-07-07T08:31:27Z | |
| dc.date.available | 2026-07-07T08:31:27Z | |
| dc.description | Discrete Bayesian Networks have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises the class of discrete BN models. It provides a flexible and expressive framework for representing and analysing the implications of causal hypotheses, expressed in terms of the effects of a manipulation of the generating underlying system. We prove that, as for a BN, identifiability analyses of causal effects can be performed through examining the topology of the CEG graph, leading to theorems analogous to the back-door theorem for the BN. | |
| dc.description | 49 pages, 18 figures | |
| dc.identifier | https://arxiv.org/abs/0709.3380 | |
| dc.identifier | http://arxiv.org/abs/0709.3380 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/138504 | |
| dc.subject | Methodology | |
| dc.title | The causal manipulation of chain event graphs | |
| dc.type | text |