Identifiability of Causal Effects in a Multi-Agent Causal Model

This paper introduces an algorithm that investigates whether the effect of an intervention is identifiable from a multi-agent causal model. A multi-agent causal model consists of a collection of agents each having access to a nondisjoint subset of the variables constituting the domain. Every agent has a causal model, determined by nonexperimental data and an acyclic causal diagram over its variables. Since in some cases nonexperimental data can be explained by more than one causal model, the effect of an intervention can not necessarily be calculated. The algorithm under investigation in this paper tests whether the assumptions made in a causal model are sufficient to calculate the effect of an intervention (i.e. whether the effect of an intervention is identifiable). It is a distributed algorithm with a minimum amount of inter-agent communication concerning solely shared variables and where the local causal models of each agent are kept confidential.