This work addresses the challenge of uplift estimation in multi-treatment scenarios on biased data, such as recommendation systems. We propose an evaluation protocol to measure the impact of non-random assignment bias on multi-treatment uplift methods and analyze their performances. The results indicate different behaviors of uplift models leading to several messages to deal with biased data.