  Uplift is a particular case of conditional treatment effect modeling. Such
models deal with cause-and-effect inference for a specific factor, such as a
marketing intervention or a medical treatment. In practice, these models are
built on individual data from randomized clinical trials where the goal is to
partition the participants into heterogeneous groups depending on the uplift.
Most existing approaches are adaptations of random forests for the uplift case.
Several split criteria have been proposed in the literature, all relying on
maximizing heterogeneity. However, in practice, these approaches are prone to
overfitting. In this work, we bring a new vision to uplift modeling. We propose
a new loss function defined by leveraging a connection with the Bayesian
interpretation of the relative risk. Our solution is developed for a specific
twin neural network architecture allowing to jointly optimize the marginal
probabilities of success for treated and control individuals. We show that this
model is a generalization of the uplift logistic interaction model. We modify
the stochastic gradient descent algorithm to allow for structured sparse
solutions. This helps training our uplift models to a great extent. We show our
proposed method is competitive with the state-of-the-art in simulation setting
and on real data from large scale randomized experiments.
