Inference in multi-agent causal models

In this article we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform causal inference, i.e. the calculation of the causal effect of one variable on other variables. We treat a state-of-the-art algorithm for performing causal inference that is based on a new factorization of the joint probability distribution and is a systematic approach for the calculation due to Tian and Pearl. We elaborate on the problems that can arise when working with a centralized approach and discuss how a decentralized cooperative multi-agent approach might overcome some of these problems. The main contribution of this article is the introduction of multi-agent causal models as a way to overcome the problems in a centralized setting. They are an extension of causal Bayesian networks to a distributed setting consisting of a number of agents each having access to an overlapping set of the variables. We extend a stateof-the-art causal inference algorithm for this particular domain. We will show that our approach is as powerful in computing causal effects as the centralized algorithm.