# Uplift Modeling with Multiple Treatments and General Response Types

arXiv: https://arxiv.org/abs/1705.08492 · HTML: https://mlsanity.com/paper/1705.08492/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 30 of 41)

- [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.01939/) (2022)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing](https://mlsanity.com/paper/2211.15728/) (2022)
- [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)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (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)
- [Decision Focused Causal Learning for Direct Counterfactual Marketing Optimization](https://mlsanity.com/paper/2407.13664/) (2024)
- [Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI](https://mlsanity.com/paper/2204.01702/) (2022)
- [M3TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling](https://mlsanity.com/paper/2401.14426/) (2024)
- [Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models](https://mlsanity.com/paper/2305.03829/) (2023)
- [Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization](https://mlsanity.com/paper/2310.04693/) (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)
- [GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace](https://mlsanity.com/paper/2203.10975/) (2022)
- [Uplift vs. predictive modeling: a theoretical analysis](https://mlsanity.com/paper/2309.12036/) (2023)
- [Improve ROI with Causal Learning and Conformal Prediction](https://mlsanity.com/paper/2407.01065/) (2024)
- [Causal Inference with Complex Treatments: A Survey](https://mlsanity.com/paper/2407.14022/) (2024)
- [Interpretable Multiple Treatment Revenue Uplift Modeling](https://mlsanity.com/paper/2101.03336/) (2021)
- [Stochastic Intervention for Causal Effect Estimation](https://mlsanity.com/paper/2105.12898/) (2021)
- [Task-specific experimental design for treatment effect estimation](https://mlsanity.com/paper/2306.05484/) (2023)
- [Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy](https://mlsanity.com/paper/2405.14743/) (2024)
- [Ads Supply Personalization via Doubly Robust Learning](https://mlsanity.com/paper/2410.12799/) (2024)
- [Bi-Level Decision-Focused Causal Learning for Large-Scale Marketing Optimization: Bridging Observational and Experimental Data](https://mlsanity.com/paper/2510.19517/) (2025)
- [Stochastic Intervention for Causal Inference via Reinforcement Learning](https://mlsanity.com/paper/2105.13514/) (2021)
- [SACO: Sequence-Aware Constrained Optimization Framework for Coupon Distribution in E-commerce](https://mlsanity.com/paper/2508.09198/) (2025)
- [Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System](https://mlsanity.com/paper/2602.18640/) (2026)

## Abstract (arXiv)

Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.