Abstract
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.
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Notes
- 1.
this dataset will be released shortly at http://research.criteo.com/outreach.
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Betlei, A., Diemert, E., Amini, MR. (2018). Uplift Prediction with Dependent Feature Representation in Imbalanced Treatment and Control Conditions. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_5
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DOI: https://doi.org/10.1007/978-3-030-04221-9_5
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