Uplift Modeling measures the impact of an action (marketing, medical treatment) on a person’s behavior. This allows the selection of the subgroup of persons for which the effect of the action will be most noteworthy. Uplift estimation is based on groups of people who have received different treatments. These groups are assumed to be equivalent. However, in practice, we observe biases between these groups. We propose in this paper a protocol to evaluate and study the impact of the Non-Random Assignment bias (NRA) on the performance of the main uplift methods. Then we present a weighting method to reduce the effect of the NRA bias. Experimental results show that our bias reduction method significantly improves the performance of uplift models under NRA bias.