Uplift Modeling: from Causal Inference to Personalization

TL;DR

This paper reviews uplift modeling, a machine learning approach for estimating individual causal effects to optimize personalized treatments and promotions, highlighting recent techniques, applications, and implementation challenges.

Contribution

It provides a comprehensive overview of state-of-the-art uplift modeling methods, their advantages, limitations, and real-world applications in personalization and marketing.

Findings

Uplift modeling effectively improves personalized marketing strategies.

Different approaches have unique advantages and limitations.

Real-life applications demonstrate practical benefits and challenges.

Abstract

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.