This paper is a first step to extending Judea Pearl's work on identification of causal effects to a multi-agent context. We introduce multi-agent causal models consisting of a collection of agents each having access to a non-disjoint 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. 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 details of each local causal model are kept confidential.