# Learning to rank for uplift modeling

arXiv: https://arxiv.org/abs/2002.05897 · HTML: https://mlsanity.com/paper/2002.05897/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling](https://mlsanity.com/paper/2111.10106/) (2021)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [A new perspective on classification: optimally allocating limited resources to uncertain tasks](https://mlsanity.com/paper/2202.04369/) (2022)
- [Optimizing Treatment Allocation in the Presence of Interference](https://mlsanity.com/paper/2410.00075/) (2024)
- [Uplift modeling with continuous treatments: A predict-then-optimize approach](https://mlsanity.com/paper/2412.09232/) (2024)
- [Uplift vs. predictive modeling: a theoretical analysis](https://mlsanity.com/paper/2309.12036/) (2023)
- [To do or not to do: cost-sensitive causal decision-making](https://mlsanity.com/paper/2101.01407/) (2021)
- [Decision-centric fairness: Evaluation and optimization for resource allocation problems](https://mlsanity.com/paper/2504.20642/) (2025)
- [Multi-channel Uplift Policy Learning](https://mlsanity.com/paper/2607.28182/) (2026)
- [Improving uplift model evaluation on RCT data](https://mlsanity.com/paper/2210.02152/) (2022)
- [The impact of heteroskedasticity on uplift modeling](https://mlsanity.com/paper/2312.05234/) (2023)
- [Multiple Instance Learning for Uplift Modeling](https://mlsanity.com/paper/2312.09639/) (2023)
- [Causal Post-Processing of Predictive Models](https://mlsanity.com/paper/2406.09567/) (2024)
- [The Estimation of Continual Causal Effect for Dataset Shifting Streams](https://mlsanity.com/paper/2504.20471/) (2025)
- [Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health](https://mlsanity.com/paper/2506.19049/) (2025)
- [VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales](https://mlsanity.com/paper/2604.02472/) (2026)
- [From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation](https://mlsanity.com/paper/2608.10182/) (2026)

## Abstract (arXiv)

Uplift modeling has effectively been used in fields such as marketing and customer retention, to target those customers that are most likely to respond due to the campaign or treatment. Uplift models produce uplift scores which are then used to essentially create a ranking. We instead investigate to learn to rank directly by looking into the potential of learning-to-rank techniques in the context of uplift modeling. We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework. Additionally, we introduce a new metric for learning-to-rank that focusses on optimizing the area under the uplift curve called the promoted cumulative gain (PCG). We employ the learning-to-rank technique LambdaMART to optimize the ranking according to PCG and show improved results over standard learning-to-rank metrics and equal to improved results when compared with state-of-the-art uplift modeling. Finally, we show how learning-to-rank models can learn to optimize a certain targeting depth, however, these results do not generalize on the test set.