A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
This work addresses the challenge of individualized treatment effect estimation in fields like marketing and healthcare, but it is incremental as it builds on existing adaptation techniques.
The paper tackled the problem of model adaptation strategies for multi-treatment uplift modeling, finding that existing adaptations fail under various data characteristics, and proposed Orthogonal Function Adaptation (OFA), which significantly improves performance and robustness compared to other methods.
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.