提出新方法提升多治疗场景下精准干预效果的建模能力
A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
- 基于函数逼近理论设计正交函数适配新策略
- 在噪声数据和观察性数据混合场景下性能显著优于传统方法
- 适合需要高鲁棒性的营销与医疗个性化决策场景
Uplift建模已成为个体化治疗效应估计的关键技术，尤其在营销和医疗领域。多治疗场景下的uplift建模对实际应用至关重要。当前方法大多从二元治疗工作迁移而来。本文将现有模型适配策略分为结构适配和特征适配两类。实验证明，这两类方法在不同数据特性（如噪声数据、混合观察数据）下均难以保持有效性。为此，我们提出基于函数逼近定理的正交函数适配（OFA）方法，以增强估计能力和鲁棒性。通过多组具有不同数据特性的实验，验证了所提方法在性能和鲁棒性上均显著优于其他基础适配方法。
原文摘要 · Abstract (English)
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.
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