Publications

Causal and predictive modeling of customer churn: lessons learned from empirical and theoretical research
Théo Verhelst, supervised by Gianluca Bontempi

PhD thesis, Université Libre de Bruxelles, 2024
PDF – 

@thesis{verhelst2024causal,
  type = {phdthesis},
  title = {Causal and Predictive Modeling of Customer Churn: Lessons Learned from Empirical and Theoretical Research},
  shorttitle = {Causal and Predictive Modeling of Customer Churn},
  author = {Verhelst, Théo},
  date = {2024-01},
  institution = {{Université libre de Bruxelles}},
  location = {{Bruxelles}},
  pagetotal = {225}
}

Uplift vs. predictive modeling: a theoretical analysis
Théo Verhelst, Robin Petit, Wouter Verbeke, Gianluca Bontempi

Preprint, 2023
arXiv – 

@misc{verhelst2023uplift,
  title={Uplift vs. predictive modeling: a theoretical analysis},
  author={Verhelst, Théo and Petit, Robin and Verbeke, Wouter and Bontempi, Gianluca},
  year={2023},
  eprint={2309.12036},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling
Théo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi

Conference paper, ECML PKDD 2023 Workshops - Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making, 2023
Workshop page – arXiv – 

@misc{verhelst2023churn,
  title={A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling},
  author={Verhelst, Théo and Mercier, Denis and Shrestha, Jeevan and Bontempi, Gianluca},
  year={2023},
  eprint={2312.07206},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
Théo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi

Journal paper, Machine Learning, 2023
Full paper – Read-only full text – arXiv – 

@article{verhelst2023partial,
  title={Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment},
  author={Verhelst, Th{\'e}o and Mercier, Denis and Shrestha, Jeevan and Bontempi, Gianluca},
  journal={Machine Learning},
  pages={1--25},
  year={2023},
  publisher={Springer}
}

Predicting reach to find persuadable customers: improving uplift models for churn prevention
Théo Verhelst, Denis Mercier, Jeevan Shrestha, Jean-Christophe Dewitte, Gianluca Bontempi

Conference paper, 24th International Conference on Discovery Science, 2021
Full paper – 

@InProceedings{verhelst2021predicting
  author={Verhelst, Th{\'e}o and Shrestha, Jeevan and Mercier, Denis and Dewitte, Jean-Christophe and Bontempi, Gianluca},
  editor={Soares, Carlos and Torgo, Luis},
  title="Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention",
  booktitle="Discovery Science",
  year="2021",
  publisher="Springer International Publishing",
  address="Cham",
  pages="44--54",
  isbn="978-3-030-88942-5"
}

Transfer learning strategies for credit card fraud detection
Bertrand Lebichot, Théo Verhelst, Yann-Aël Le Borgne, Liyun He-Guelton, Frédéric Oblé, Gianluca Bontempi

Journal paper, IEEE Access, 2021
Full paper – 

@article{lebichot2021transfer,
  author={Lebichot, Bertrand and Verhelst, Théo and Le Borgne, Yann-Aël and He-Guelton, Liyun and Oblé, Frédéric and Bontempi, Gianluca},
  journal={IEEE Access},
  title={Transfer Learning Strategies for Credit Card Fraud Detection},
  year={2021},
  volume={9},
  number={},
  pages={114754-114766},
  doi={10.1109/ACCESS.2021.3104472}
}

Understanding telecom customer churn with machine learning: from prediction to causal inference
Théo Verhelst, Olivier Caelen, Jean-Christophe Dewitte, Bertrand Lebichot, Gianluca Bontempi

Conference paper, 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, 2019
Full paper – 

@InProceedings{verhelst2021understanding,
  author={Verhelst, Th{\'e}o and Caelen, Olivier and Dewitte, Jean-Christophe and Lebichot, Bertrand and Bontempi, Gianluca},
  editor={Bogaerts, Bart and Bontempi, Gianluca and Geurts, Pierre and Harley, Nick and Lebichot, Bertrand and Lenaerts, Tom and Louppe, Gilles},
  title="Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference",
  booktitle="Artificial Intelligence and Machine Learning",
  year="2020",
  publisher="Springer International Publishing",
  address="Cham",
  pages="182--200",
  isbn="978-3-030-65154-1"
}

Churn Prediction and Causal Analysis on Telecom Customer Data
Théo Verhelst, supervised by Gianluca Bontempi

Master thesis, Université Libre de Bruxelles, 2018
PDF – 

@thesis{verhelst2018churn,
  type = {mathesis},
  title = {Churn Prediction and Causal Analysis on Telecom Customer Data},
  author = {Verhelst, Théo},
  date = {2018},
  institution = {{Université libre de Bruxelles}},
  location = {{Brussels, Belgium}},
  url = {http://127.0.0.1:4000/assets/documents/theo_verhelst_master_thesis.pdf},
  urldate = {2024-04-09},
  pagetotal = {83}
}