UTBoost: A Tree-boosting based System for Uplift Modeling
Junjie Gao Xiangyu Zheng DongDong Wang Zhixiang Huang Bangqi Zheng Kai Yang
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.