  Uplift modeling is an area of machine learning which aims at predicting the
causal effect of some action on a given individual. The action may be a medical
procedure, marketing campaign, or any other circumstance controlled by the
experimenter. Building an uplift model requires two training sets: the
treatment group, where individuals have been subject to the action, and the
control group, where no action has been performed. An uplift model allows then
to assess the gain resulting from taking the action on a given individual, such
as the increase in probability of patient recovery or of a product being
purchased. This paper describes an adaptation of the well-known boosting
techniques to the uplift modeling case. We formulate three desirable properties
which an uplift boosting algorithm should have. Since all three properties
cannot be satisfied simultaneously, we propose three uplift boosting
algorithms, each satisfying two of them. Experiments demonstrate the usefulness
of the proposed methods, which often dramatically improve performance of the
base models and are thus new and powerful tools for uplift modeling.
