M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911764790116352 |
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| author | Sun, Zexu Chen, Xu |
| author_facet | Sun, Zexu Chen, Xu |
| contents | 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 \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). M$^3$TN 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 M$^3$TN. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_14426 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling Sun, Zexu Chen, Xu Machine Learning Artificial Intelligence Methodology 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 \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). M$^3$TN 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 M$^3$TN. |
| title | M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2401.14426 |