# Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth

arXiv: https://arxiv.org/abs/2408.12803 · HTML: https://mlsanity.com/paper/2408.12803/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [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)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Learning to Branch for Multi-Task Learning](https://mlsanity.com/paper/2006.01895/) (2020)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [On Inductive Biases for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2106.03765/) (2021)
- [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)
- [Uplift Modeling for Multiple Treatments with Cost Optimization](https://mlsanity.com/paper/1908.05372/) (2019)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing](https://mlsanity.com/paper/2402.03379/) (2024)
- [M3TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling](https://mlsanity.com/paper/2401.14426/) (2024)
- [Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey](https://mlsanity.com/paper/2305.13843/) (2023)
- [A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling](https://mlsanity.com/paper/1709.03683/) (2017)

## Built on by (most cited first, 5 of 5)

- [Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System](https://mlsanity.com/paper/2602.18640/) (2026)
- [TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis](https://mlsanity.com/paper/2504.18881/) (2025)
- [KLAN: Kuaishou Landing-page Adaptive Navigator](https://mlsanity.com/paper/2507.23459/) (2025)
- [Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay](https://mlsanity.com/paper/2605.00374/) (2026)
- [FunnelCausalNet: Funnel-aware Joint Conversion-Revenue Uplift for Multi-tier Coupon Allocation](https://mlsanity.com/paper/2608.11675/) (2026)

## Abstract (arXiv)

As a key component in boosting online user growth, uplift modeling aims to measure individual user responses (e.g., whether to play the game) to various treatments, such as gaming bonuses, thereby enhancing business outcomes. However, previous research typically considers a single-task, single-treatment setting, where only one treatment exists and the overall treatment effect is measured by a single type of user response. In this paper, we propose a Multi-Treatment Multi-Task (MTMT) uplift network to estimate treatment effects in a multi-task scenario. We identify the multi-treatment problem as a causal inference problem with a tiered response, comprising a base effect (from offering a treatment) and an incremental effect (from offering a specific type of treatment), where the base effect can be numerically much larger than the incremental effect. Specifically, MTMT separately encodes user features and treatments. The user feature encoder uses a multi-gate mixture of experts (MMOE) network to encode relevant user features, explicitly learning inter-task relations. The resultant embeddings are used to measure natural responses per task. Furthermore, we introduce a treatment-user feature interaction module to model correlations between each treatment and user feature. Consequently, we separately measure the base and incremental treatment effect for each task based on the produced treatment-aware representations. Experimental results based on an offline public dataset and an online proprietary dataset demonstrate the effectiveness of MTMT in single/multi-treatment and single/multi-task settings. Additionally, MTMT has been deployed in our gaming platform to improve user experience.