Many real world learning problems are best characterized by an interaction of multiple independent causes or factors. Discover(cid:173) ing such causal structure from the data is the focus of this paper. Based on Zemel and Hinton's cooperative vector quantizer (CVQ) architecture, an unsupervised learning algorithm is derived from the Expectation-Maximization (EM) framework. Due to the com(cid:173) binatorial nature of the data generation process, the exact E-step is computationally intractable. Two alternative methods for com(cid:173) puting the E-step are proposed: Gibbs sampling and mean-field approximation, and some promising empirical results are presented.