In this manuscript (ms), we propose causal inference based single-branch ensemble trees for uplift modeling, namely CIET. Different from standard classification methods for predictive probability modeling, CIET aims to achieve the change in the predictive probability of outcome caused by an action or a treatment. According to our CIET, two partition criteria are specifically designed to maximize the difference in outcome distribution between the treatment and control groups. Next, a novel single-branch tree is built by taking a top-down node partition approach, and the remaining samples are censored since they are not covered by the upper node partition logic. Repeating the tree-building process on the censored data, single-branch ensemble trees with a set of inference rules are thus formed. Moreover, CIET is experimentally demonstrated to outperform previous approaches for uplift modeling in terms of both area under uplift curve (AUUC) and Qini coefficient significantly. At present, CIET has already been applied to online personal loans in a national financial holdings group in China. CIET will also be of use to analysts applying machine learning techniques to causal inference in broader business domains such as web advertising, medicine and economics.

翻译：本文提出基于因果推断的单分支集成树用于提升建模，即CIET。与用于预测概率建模的标准分类方法不同，CIET旨在实现由某一行动或处理所导致的预测结果概率的变化。根据所提出的CIET，我们专门设计了两种划分准则，以最大化处理组与对照组之间结果分布的差异。接下来，通过采用自上而下的节点划分方式构建一种新颖的单分支树，由于未被上层节点划分逻辑覆盖，剩余样本被截尾处理。在截尾数据上重复构建树的过程，从而形成一组具有推理规则的单分支集成树。此外，实验证明，CIET在提升曲线下面积（AUUC）和Qini系数方面均显著优于先前的提升建模方法。目前，CIET已应用于中国某国家金融控股集团的在线个人贷款业务中。CIET还将有助于在更广泛的商业领域（如网络广告、医学和经济学）中应用机器学习技术进行因果推断的分析人员。