A procedure is proposed to test for the existence of a fully causal relationship between two variables. The method involves contrasting the probabilistic forecasting performance of a univariate and bivariate specification for the same variable Y. If there exists some theory or belief that X causes Y, and the addition of a variable X to the information set of a prequential forecasting system for a variable Y reduces miscalibration and/or the level of forecast uncertainty with respect Y's distribution for the next period, then a fully causal effect running from X to Y may be inferred. Vector autoregression allows testing for feedback. The method is to be applied to the issue of causality between the live cattle futures market and a major slaughter cattle cash market. Copyright 1992 by MIT Press.