We describe statistical models for detecting causality between two events. Our models are kinds of latent variable models,
actually expanded versions of the existing statistical co-occurrence models. The (statistical) dependency information between
two events needs to be incorporated into causal models. We handle this information via latent variables in our models. Through
experiments, we achieved .678 F-measure value for the evaluation data.