Uplift Modeling: from Causal Inference to Personalization
This tutorial provides an incremental overview of existing methods for personalization in online platforms, targeting practitioners and researchers in causal inference.
The paper addresses the problem of estimating causal effects for personalized treatments in e-commerce, presenting a tutorial on uplift modeling techniques to optimize business metrics like promotional campaigns.
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.