Uplift modeling aims to estimate the incremental impact of a treatment, such as a marketing campaign or a drug, on an individual's outcome. Bank or Telecom uplift data often have hundreds to thousands of features. In such situations, detection of irrelevant features is an essential step to reduce computational time and increase model performance. We present a parameter-free feature selection method for uplift modeling founded on a Bayesian approach. we describe a parameter-free feature selection method for uplift. Experiments show that the new method both removes irrelevant features and achieves better performances than state of the art methods.