Identification of Causal Effects in Multi-Agent Causal Models.
Sam Maes, Stijn Meganck, Bernard Manderick · 2005
In this paper we introduce multi-agent causal models (MACMs) which are an extension of causal Bayesian networks to a multi-agent setting. Instead of 1 single agent modeling the entire domain, there are several agents each modeling non-disjoint subsets of the domain. Every agent has a causal model, determined by an acyclic causal diagram and a joint probability distribution over its observed variables.