  Uplift modeling is a machine learning technique that aims to model treatment
effects heterogeneity. It has been used in business and health sectors to
predict the effect of a specific action on a given individual. Despite its
advantages, uplift models show high sensitivity to noise and disturbance, which
leads to unreliable results. In this paper we show different approaches to
address the problem of uplift modeling, we demonstrate how disturbance in data
can affect uplift measurement. We propose a new approach, we call it
Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared
our approach with the existing uplift methods, on simulated and real data-sets.
The experiments show that our approach outperforms the existing approaches,
especially in the case of high noise data environment.
