  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.
