  We consider the task of optimizing treatment assignment based on individual
treatment effect prediction. This task is found in many applications such as
personalized medicine or targeted advertising and has gained a surge of
interest in recent years under the name of Uplift Modeling. It consists in
targeting treatment to the individuals for whom it would be the most
beneficial. In real life scenarios, when we do not have access to ground-truth
individual treatment effect, the capacity of models to do so is generally
measured by the Area Under the Uplift Curve (AUUC), a metric that differs from
the learning objectives of most of the Individual Treatment Effect (ITE)
models. We argue that the learning of these models could inadvertently degrade
AUUC and lead to suboptimal treatment assignment. To tackle this issue, we
propose a generalization bound on the AUUC and present a novel learning
algorithm that optimizes a derivable surrogate of this bound, called AUUC-max.
Finally, we empirically demonstrate the tightness of this generalization bound,
its effectiveness for hyper-parameter tuning and show the efficiency of the
proposed algorithm compared to a wide range of competitive baselines on two
classical benchmarks.
