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.

翻译：提升建模是一类用于估计个体或子群体层面上预处理的因果效应的机器学习技术。近年来，因果关系与提升建模已成为在线电商平台个性化领域的关键趋势，能够为每位用户选择最优处理方案以最大化目标业务指标。提升建模在个性化促销活动中尤其有用，因为需要权衡促销带来的潜在收益与成本。本教程将涵盖因果关系的基本概念，并向听众介绍提升建模领域的最新前沿技术。我们将讨论不同方法的优势与局限性，并深入探讨约束提升建模的特殊框架。最后，我们将展示实际应用案例，并讨论在工业生产环境中实现这些模型所面临的挑战。