Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.04315 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916933933203456 |
|---|---|
| author | Liu, Yang Yuan, Chaoyu |
| author_facet | Liu, Yang Yuan, Chaoyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | We Have It Covered: A Resampling-based Method for Uplift Model Comparison Liu, Yang Yuan, Chaoyu Methodology Machine Learning 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. |
| title | We Have It Covered: A Resampling-based Method for Uplift Model Comparison |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2509.04315 |