Abstract
Uplift modeling refers to approaches to quantify net difference in outcome between applying a treatment and not applying it to an individual. It is a typical causal inference problem which allowing us to design a refined decision rule that only targets those susceptible. The core difficulty of the existing methods is that there is no direct supervision label for uplifts. In this paper, we propose an efficient Causal Enhanced Uplift Model (CEUM) to excavate potential uplift information. Specifically, we first construct contrastive pairs following the properties of partial order relation of uplifts and maintain the structural correlation and consistency during training. Then, we seek for a promising average causal effect in batches to approach the mean of individual estimated uplift. Finally, we benchmark the efficacy of the proposed method by conducting comprehensive experiments and the results show that CEUM achieves the state-of-the-art performance on two real-world datasets.
X. He and G. Xu contribute equally to this work.
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He, X. et al. (2022). Causal Enhanced Uplift Model. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_10
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