mlsanityData to 7 Oct 2026

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

arXiv 2401.14426 ↗First posted 24 Jan 2024cs.LG cs.AI stat.MEcited by 6

On the map: Mixture-of-experts → MoE large language models

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.

Key facts. Cited by 6 papers in the map (data through 7 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.

Builds on

15 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer23 Jan

Other references · 14

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  5. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  6. 2013Learning Factored Representations in a Deep Mixture of Expertscited by 275
  7. 2017Uplift Modeling with Multiple Treatments and General Response Typescited by 41
  8. 2020A unified survey of treatment effect heterogeneity modeling and uplift modelingcited by 36
  9. 2023Explicit Feature Interaction-aware Uplift Network for Online Marketingcited by 25
  10. 2019Qini-based Uplift Regressioncited by 7
  11. 2023Offline Imitation Learning with Variational Counterfactual Reasoningcited by 7
  12. 2018Modified Causal Forests for Estimating Heterogeneous Causal Effectscited by 5
2 more
  1. 2023Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitizationcited by 5
  2. 2022GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplacecited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growthcited by 5
  2. 2025Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketingcited by 3
  3. 2026Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking Systemcited by 1
  4. 2026GUSH3R: Everyone Everywhere All at Once as Gaussianscited by 1
  5. 2025KLAN: Kuaishou Landing-page Adaptive Navigatorcited by 0
  6. 2025Cross-Treatment Effect Estimation for Multi-Category, Multi-Valued Causal Inference via Dynamic Neural Maskingcited by 0

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