# Uplift Modeling for Multiple Treatments with Cost Optimization

arXiv: https://arxiv.org/abs/1908.05372 · HTML: https://mlsanity.com/paper/1908.05372/

## Key facts

- Cited by 25 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 25 of 25)

- [E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling](https://mlsanity.com/paper/2108.13298/) (2021)
- [LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm](https://mlsanity.com/paper/2201.12585/) (2022)
- [DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2207.09920/) (2022)
- [Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects](https://mlsanity.com/paper/2205.14714/) (2022)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint](https://mlsanity.com/paper/2302.04477/) (2023)
- [Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI](https://mlsanity.com/paper/2204.01702/) (2022)
- [Uplift Modeling: from Causal Inference to Personalization](https://mlsanity.com/paper/2308.09066/) (2023)
- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Machine learning reveals how personalized climate communication can both succeed and backfire](https://mlsanity.com/paper/2109.05104/) (2021)
- [Generalized Causal Tree for Uplift Modeling](https://mlsanity.com/paper/2202.02416/) (2022)
- [Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions](https://mlsanity.com/paper/2306.13759/) (2023)
- [To do or not to do: cost-sensitive causal decision-making](https://mlsanity.com/paper/2101.01407/) (2021)
- [Interpretable Multiple Treatment Revenue Uplift Modeling](https://mlsanity.com/paper/2101.03336/) (2021)
- [Distribution-consistency Structural Causal Models](https://mlsanity.com/paper/2401.15911/) (2024)
- [Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling](https://mlsanity.com/paper/2302.01563/) (2023)
- [Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques](https://mlsanity.com/paper/2408.13628/) (2024)
- [Assessing the Impact of Upselling in Online Fantasy Sports](https://mlsanity.com/paper/2409.00629/) (2024)
- [Segment Discovery: Enhancing E-commerce Targeting](https://mlsanity.com/paper/2409.13847/) (2024)
- [Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health](https://mlsanity.com/paper/2506.19049/) (2025)
- [A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap](https://mlsanity.com/paper/2512.03060/) (2025)
- [Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy](https://mlsanity.com/paper/2512.19805/) (2025)
- [Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making](https://mlsanity.com/paper/2604.26169/) (2026)
- [Multi-channel Uplift Policy Learning](https://mlsanity.com/paper/2607.28182/) (2026)

## Abstract (arXiv)

Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from a treatment and then prioritize delivering or promoting the preferred experience to those users. An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously. In this paper, we extend standard uplift models to support multiple treatment groups with different costs. We evaluate the performance of the proposed models using both synthetic and real data. We also describe a production implementation of the approach.