Uplift Modeling with High Class Imbalance
Abstract
Uplift modeling refers to estimating the causal effect of a treatment on an individual observation, used for instance to identify customers worth targeting with a discount in e-commerce. We introduce a simple yet effective undersampling strategy for dealing with the prevalent problem of high class imbalance (low conversion rate) in such applications. Our strategy is agnostic to the base learners and produces a 6.5% improvement over the best published benchmark for the largest public uplift data which incidentally exhibits high class imbalance. We also introduce a new metric on calibration for uplift modeling and present a strategy to improve the calibration of the proposed method.
Cite
Text
Nyberg et al. "Uplift Modeling with High Class Imbalance." Proceedings of The 13th Asian Conference on Machine Learning, 2021.Markdown
[Nyberg et al. "Uplift Modeling with High Class Imbalance." Proceedings of The 13th Asian Conference on Machine Learning, 2021.](https://mlanthology.org/acml/2021/nyberg2021acml-uplift/)BibTeX
@inproceedings{nyberg2021acml-uplift,
title = {{Uplift Modeling with High Class Imbalance}},
author = {Nyberg, Otto and Kuśmierczyk, Tomasz and Klami, Arto},
booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
year = {2021},
pages = {315-330},
volume = {157},
url = {https://mlanthology.org/acml/2021/nyberg2021acml-uplift/}
}