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.

利用提升建模以个体或子群组级别估计治疗效果的机器学习技术，是在线电子商务平台个性化的关键趋势之一，它能够选择最佳的治疗方案以最大化目标业务指标。研究探讨了引起潜在效益和潜在成本之间权衡的个性化促销活动，并介绍了新兴的提升建模技术。还讨论了不同方法的优势和局限，并深入探讨了约束提升建模的独特设置。最后，提出了实际应用和在实施这些模型时面临的挑战。