Uplift Modeling: from Causal Inference to Personalization
Felipe Moraes Hugo Manuel Proença Anastasiia Kornilova Javier Albert Dmitri Goldenberg
Uplift modeling is a collection of machine learning techniques for estimating
causal effects of a treatment at the individual or subgroup levels. Over the
last years, causality and uplift modeling have become key trends in
personalization at online e-commerce platforms, enabling the selection of the
best treatment for each user in order to maximize the target business metric.
Uplift modeling can be particularly useful for personalized promotional
campaigns, where the potential benefit caused by a promotion needs to be
weighed against the potential costs. In this tutorial we will cover basic
concepts of causality and introduce the audience to state-of-the-art techniques
in uplift modeling. We will discuss the advantages and the limitations of
different approaches and dive into the unique setup of constrained uplift
modeling. Finally, we will present real-life applications and discuss
challenges in implementing these models in production.