# Qini-based Uplift Regression

arXiv: https://arxiv.org/abs/1911.12474 · HTML: https://mlsanity.com/paper/1911.12474/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [M3TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling](https://mlsanity.com/paper/2401.14426/) (2024)
- [Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization](https://mlsanity.com/paper/2310.04693/) (2023)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web](https://mlsanity.com/paper/2602.10502/) (2026)
- [Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness](https://mlsanity.com/paper/2603.20775/) (2026)
- [A Twin Neural Model for Uplift](https://mlsanity.com/paper/2105.05146/) (2021)
- [A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling](https://mlsanity.com/paper/2511.01185/) (2025)

## Abstract (arXiv)

Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. We introduce a Qini-based uplift regression model to analyze a large insurance company's retention marketing campaign. Our approach is based on logistic regression models. We show that a Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression. This results in interpretable parsimonious models with few relevant xplanatory variables. Our results show that performing Qini-based parameters estimation significantly improves the uplift models performance.