A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling

TL;DR

This paper investigates current strategies for multi-treatment uplift modeling, categorizes them, and introduces a new Orthogonal Function Adaptation method that improves robustness and performance across various data conditions.

Contribution

The paper categorizes existing adaptation techniques and proposes OFA, a novel method that enhances robustness and effectiveness in multi-treatment uplift modeling.

Findings

OFA significantly outperforms existing adaptation methods.

OFA exhibits the highest robustness across data variations.

Current adaptation methods struggle with noisy and observational data.

Abstract

Uplift modeling has emerged as a crucial technique for individualized treatment effect estimation, particularly in fields such as marketing and healthcare. Modeling uplift effects in multi-treatment scenarios plays a key role in real-world applications. Current techniques for modeling multi-treatment uplift are typically adapted from binary-treatment works. In this paper, we investigate and categorize all current model adaptations into two types: Structure Adaptation and Feature Adaptation. Through our empirical experiments, we find that these two adaptation types cannot maintain effectiveness under various data characteristics (noisy data, mixed with observational data, etc.). To enhance estimation ability and robustness, we propose Orthogonal Function Adaptation (OFA) based on the function approximation theorem. We conduct comprehensive experiments with multiple data characteristics to study the effectiveness and robustness of all model adaptation techniques. Our experimental results demonstrate that our proposed OFA can significantly improve uplift model performance compared to other vanilla adaptation methods and exhibits the highest robustness.