# Interpretable Multiple Treatment Revenue Uplift Modeling

arXiv: https://arxiv.org/abs/2101.03336 · HTML: https://mlsanity.com/paper/2101.03336/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 9 Jan 2021.

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

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [Estimation of causal effects with multiple treatments: a review and new ideas](https://mlsanity.com/paper/1701.05132/) (2017)
- [Uplift Modeling for Multiple Treatments with Cost Optimization](https://mlsanity.com/paper/1908.05372/) (2019)
- [Response Transformation and Profit Decomposition for Revenue Uplift Modeling](https://mlsanity.com/paper/1911.08729/) (2019)

## Built on by (most cited first, 2 of 2)

- [Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing](https://mlsanity.com/paper/2405.15301/) (2024)
- [VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales](https://mlsanity.com/paper/2604.02472/) (2026)

## Abstract (arXiv)

Big data and business analytics are critical drivers of business and societal transformations. Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. Prior work examines models for single treatments and binary customer responses. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes. This facilitates selecting an optimal treatment from a set of alternatives and estimating treatment effects in the form of business outcomes of continuous scale. Another contribution emerges from an evaluation of an uplift model's interpretability, whereas prior studies focus almost exclusively on predictive performance. To achieve these goals, the paper develops revenue uplift models for multiple treatments based on a recently introduced algorithm for causal machine learning, the causal forest. Empirical experimentation using two real-world marketing data sets demonstrates the advantages of the proposed modeling approach over benchmarks and standard marketing practices.