Uplift modeling refers to the set of machine learning techniques that a
manager may use to estimate customer uplift, that is, the net effect of an
action on some customer outcome. By identifying the subset of customers for
whom a treatment will have the greatest effect, uplift models assist
decision-makers in optimizing resource allocations and maximizing overall
returns. Accurately estimating customer uplift poses practical challenges, as
it requires assessing the difference between two mutually exclusive outcomes
for each individual. In this paper, we propose two innovative adaptations of
the well-established Gradient Boosting Decision Trees (GBDT) algorithm, which
learn the causal effect in a sequential way and overcome the counter-factual
nature. Both approaches innovate existing techniques in terms of ensemble
learning method and learning objectives, respectively. Experiments on
large-scale datasets demonstrate the usefulness of the proposed methods, which
often yielding remarkable improvements over base models. To facilitate the
application, we develop the UTBoost, an end-to-end tree boosting system
specifically designed for uplift modeling. The package is open source and has
been optimized for training speed to meet the needs of real industrial
applications.

通过创新 GBDT 算法的两种方法，即序贯学习和克服反事实性，用于 uplift modeling，该研究提出了一种能够优化资源分配、最大化整体回报的客户激励估计模型，并且在大规模数据集上的实验证明了该方法的有效性。为了方便应用，还开发了针对 uplift modeling 的具体设计的树提升系统 UTBoost。