ArXiv Preprint
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.
Fanglan Zheng, Menghan Wang, Kun Li, Jiang Tian, Xiaojia Xiang
2023-02-03
