Learning Discriminative Representation Base on Attention for Uplift

Abstract

Uplift modeling aims to estimate Conditional Average Treatment Effects (CATE) for a given factor, such as a marketing intervention or a medical treatment. Given covariates of a single subject under different treatment indicators, most of existing approaches, especially those based on deep learning, either learn the same representation from a single model or learn different representations from two separate models. Thus, these methods could not learn discriminative representations or could not utilize both information of control and treatment group. In this paper, we develop an attentive neural uplift model to alleviate the above shortcomings by utilizing attention mechanisms to map the original covariate space $\mathcal {X}$ into a latent space $\mathcal {Z}$ in a single model. Given covariates of a subject, the learned representations in space $\mathcal {Z}$ which we called after-treatment representation are discriminative under different treatment indicators, thus can model potential outcomes more effectively. Moreover, the model is trained on a single neural network so that the information shared by treatment and control group is utilised. Experiments on synthetic and real-world datasets show our proposed method is competitive with the state-of-the-art.

G. Xu and C. Yin—Equal contribution.