# We Have It Covered: A Resampling-based Method for Uplift Model Comparison

arXiv: https://arxiv.org/abs/2509.04315 · HTML: https://mlsanity.com/paper/2509.04315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing](https://mlsanity.com/paper/2405.15301/) (2024)
- [A New Transformation Approach for Uplift Modeling with Binary Outcome](https://mlsanity.com/paper/2310.05549/) (2023)

## Abstract (arXiv)

Uplift models play a critical role in modern marketing applications to help understand the incremental benefits of interventions and identify optimal targeting strategies. A variety of techniques exist for building uplift models, and it is essential to understand the model differences in the context of intended applications. The uplift curve is a widely adopted tool for assessing uplift model performance on the selection universe when observations are available for the entire population. However, when it is uneconomical or infeasible to select the entire population, it becomes difficult or even impossible to estimate the uplift curve without appropriate sampling design. To the best of our knowledge, no prior work has addressed uncertainty quantification of uplift curve estimates, which is essential for model comparisons. We propose a two-step sampling procedure and a resampling-based approach to compare uplift models with uncertainty quantification, examine the proposed method via simulations and real data applications, and conclude with a discussion.