  Uplift is a particular case of individual treatment effect modeling. Such
models deal with cause-and-effect inference for a specific factor, such as a
marketing intervention. In practice, these models are built on customer data
who purchased products or services to improve product marketing. Uplift is
estimated using either i) conditional mean regression or ii) transformed
outcome regression. Most existing approaches are adaptations of classification
and regression trees for the uplift case. However, in practice, these
conventional approaches are prone to overfitting. Here we propose a new method
using neural networks. This representation allows to jointly optimize the
difference in conditional means and the transformed outcome losses. As a
consequence, the model not only estimates the uplift, but also ensures
consistency in predicting the outcome. We focus on fully randomized
experiments, which is the case of our data. We show our proposed method
improves the state-of-the-art on synthetic and real data.
