Distributed learning of multi-agent causal models

In this paper we propose a distributed structure learning algorithm for the recently introduced multi-agent causal Models (MACMs). MACMs are an extension of causal Bayesian networks (CBN) to a distributed domain. In this setting it is assumed that there is no single database containing all the information of the domain. Instead, there are several sites holding non-disjoint subsets of the domain variables. At each site there is an agent capable of learning a local causal model. We study the possibility of combining the information of the local models into one globally consistent model. We propose an algorithm that yields the possibility to learn new local structures that can be combined to perform globally consistent causal inference.