Ecient probabilistic inference is key to the success of sta- tistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayes-ball algorithm can identify the requisite variables in a propositional Bayesian network and thus ignore irrelevant variables. This paper presents a lifted version of Bayes-ball, which works directly on the rst-order level, and shows how this algorithm applies to (lifted) inference in directed rst-order probabilistic models.