A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling

Théo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi

ECML PKDD 2023 Workshops - Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making

Workshop page

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment

Théo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi

Machine Learning, 2023

Full paper – Read-only full text – arXiv

Predicting reach to find persuadable customers: improving uplift models for churn prevention

Théo Verhelst, Denis Mercier, Jeevan Shrestha, Jean-Christophe Dewitte, Gianluca Bontempi

24th International Conference on Discovery Science (DS 2021)

Full paper

Transfer learning strategies for credit card fraud detection

Bertrand Lebichot, Théo Verhelst, Yann-Aël Le Borgne, Liyun He-Guelton, Frédéric Oblé, Gianluca Bontempi

IEEE Access, 2021

Full paper

Understanding telecom customer churn with machine learning: from prediction to causal inference

Théo Verhelst, Olivier Caelen, Jean-Christophe Dewitte, Bertrand Lebichot, Gianluca Bontempi

31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019)

Full paper

Churn Prediction and Causal Analysis on Telecom Customer Data

Théo Verhelst, supervised by Gianluca Bontempi

Université Libre de Bruxelles, 2019

Master thesis