Uplift Prediction with Dependent Feature Representation in Imbalanced Treatment and Control Conditions

  • Artem BetleiEmail author
  • Eustache Diemert
  • Massih-Reza Amini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)


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.


Uplift prediction Causal inference Digital advertising Supervised learning 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Artem Betlei
    • 1
    • 2
    Email author
  • Eustache Diemert
    • 1
  • Massih-Reza Amini
    • 2
  1. 1.Criteo ResearchGrenobleFrance
  2. 2.UGA/CNRS LIGGrenobleFrance

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