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Uplift Modeling Under Limited Supervision

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Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14946))

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Abstract

Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual intervention is a standard practice to diminish the risk. However, existing methods for treatment effect prediction tend to rely on training sets of substantial size, which are built from real experiments and are thus inherently risky to create. In this work we propose a graph neural network to diminish the required training set size, relying on graphs that are common in e-commerce data. Specifically, we view the problem as node regression with a restricted number of labeled instances, develop a two-model neural architecture akin to previous causal effect estimators, and test varying message-passing layers for encoding. Furthermore, as an extra step, we combine the model with an acquisition function to guide the creation of the training set in settings with extremely low experimental budget. The framework is flexible since each step can be used separately with other models or treatment policies. The experiments on real large-scale networks indicate a clear advantage of our methodology over the state of the art, which in many cases performs close to random, underlining the need for models that can generalize with limited supervision to reduce experimental risks.

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Notes

  1. 1.

    https://github.com/geopanag/UMGNet.

  2. 2.

    https://ods.ai/competitions/x5-retailhero-uplift-modeling.

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Acknowledgements

Supported in part by ANR (French National Research Agency) under the JCJC project GraphIA (ANR-20-CE23-0009-01).

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Authors and Affiliations

  1. University of Luxembourg, 6 avenue de la Fonte, Esch-sur-Alzette, Luxembourg

    George Panagopoulos & Jun Pang

  2. Université Paris-Saclay, CentraleSupélec, Inria, 3 Rue Joliot Curie, Gif-sur-Yvette, France

    Daniele Malitesta & Fragkiskos D. Malliaros

Authors
  1. George Panagopoulos
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  2. Daniele Malitesta
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  3. Fragkiskos D. Malliaros
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  4. Jun Pang
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Correspondence to George Panagopoulos .

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Editors and Affiliations

  1. LTCI, Télécom Paris, Palaiseau Cedex, France

    Albert Bifet

  2. KU Leuven, Leuven, Belgium

    Jesse Davis

  3. Faculty of Informatics, Vytautas Magnus University, Akademija, Lithuania

    Tomas Krilavičius

  4. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Meelis Kull

  5. Department of Computer Science, Bundeswehr University Munich, Munich, Germany

    Eirini Ntoutsi

  6. Dept. of Computer Science, University of Helsinki, Helsinki, Finland

    Indrė Žliobaitė

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This study only involved public datasets that are freely available for academic purposes. There are no obvious ethical considerations regarding negative impacts from the broader application of the method.

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Panagopoulos, G., Malitesta, D., Malliaros, F.D., Pang, J. (2024). Uplift Modeling Under Limited Supervision. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14946. Springer, Cham. https://doi.org/10.1007/978-3-031-70365-2_8

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