# M3TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling

arXiv: https://arxiv.org/abs/2401.14426 · HTML: https://mlsanity.com/paper/2401.14426/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in MoE large language models, one of 12 directions in Mixture-of-experts.
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Placed in

- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Builds on (most cited first, 15 of 15)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Qini-based Uplift Regression](https://mlsanity.com/paper/1911.12474/) (2019)
- [Offline Imitation Learning with Variational Counterfactual Reasoning](https://mlsanity.com/paper/2310.04706/) (2023)
- [Modified Causal Forests for Estimating Heterogeneous Causal Effects](https://mlsanity.com/paper/1812.09487/) (2018)
- [Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization](https://mlsanity.com/paper/2310.04693/) (2023)
- [GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace](https://mlsanity.com/paper/2203.10975/) (2022)

## Built on by (most cited first, 6 of 6)

- [Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth](https://mlsanity.com/paper/2408.12803/) (2024)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System](https://mlsanity.com/paper/2602.18640/) (2026)
- [GUSH3R: Everyone Everywhere All at Once as Gaussians](https://mlsanity.com/paper/2607.05243/) (2026)
- [KLAN: Kuaishou Landing-page Adaptive Navigator](https://mlsanity.com/paper/2507.23459/) (2025)
- [Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Masking](https://mlsanity.com/paper/2511.01641/) (2025)

## Abstract (arXiv)

Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. There are still some limitations. Firstly, existing methods calculate uplift based on predicted responses, which may not guarantee a consistent uplift distribution between treatment and control groups. Moreover, this may cause cumulative errors for multi-valued treatment. Secondly, the model parameters become numerous with many prediction heads, leading to reduced efficiency. To address these issues, we propose a novel underlineMulti-gate underlineMixture-of-Experts based underlineMulti-valued underlineTreatment underlineNetwork (M3TN). M3TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness. We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M3TN.