Uplift prediction concerns the causal impact of a treatment over individuals and it has attracted a lot of attention in the machine learning community these past years. In this paper, we consider a typical situation where the learner has access to an imbalanced treatment and control data collection affecting the performance of the existing approaches. Inspired from transfer and multi-task learning paradigms, our approach overcomes this problem by sharing the feature representation of observations. Furthermore, we provide a unified framework for the existing evaluation metrics and discuss their merits. Our experimental results, over a large-scale collection show the benefits of the proposed approaches.
CITATION STYLE
Betlei, A., Diemert, E., & Amini, M. R. (2018). Uplift prediction with dependent feature representation in imbalanced treatment and control conditions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11305 LNCS, pp. 47–57). Springer Verlag. https://doi.org/10.1007/978-3-030-04221-9_5
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