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.

This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 is robust to multiple treatment effects applied simultaneously to the same unit, continuous and binary treatments, and many covariates. We compared M3E2 with three baselines in three synthetic benchmark datasets: two with multiple treatments and one with one treatment. Our analysis showed that our method has superior performance, making more assertive estimations of the true treatment effects. The code is available at github.com/raquelaoki/M3E2.

Multi-task learning is a machine learning method in which a model learns to solve multiple tasks simultaneously. The assumption is that by learning to complete multiple correlated tasks with the same model, that the performance of each task will be higher than if we trained individual models on each task. However, this assumption does not always hold true. Naïve multi-task learning approaches do not consider the relationships between tasks and trade-offs involved in learning to complete all of the tasks. Google's multi-gate mixture-of-experts model (MMoE) attempts to improve upon the baseline multi-task learning methods by explicitly learning relationships between tasks.

I have recently read this paper from KDD 2018 and wanted to implement the paper and tried to see if I can reproduce the results. This is my first time implementing a paper and I don't think my implementation was perfect. However, I'm excited about this work and I would really appreciate it if y'all can take a look at it and give some feedback on the implementation! Please feel free to submit issues/PRs and I'm more than happy to discuss them and make the implementation better:)