论文标题
提升建模和异质治疗效果的特征选择方法
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
论文作者
论文摘要
隆升建模是一种因果学习技术，可估计亚组级别的治疗效果。它通常在行业和其他地方用于定位广告等任务。在典型的设置中，Ruplift模型可以将数千个功能作为输入，这是昂贵的，并且导致了诸如过度拟合和模型可解释性差的问题。因此，需要选择建模最重要的特征的子集。但是，进行功能选择的传统方法不适合该任务，因为它们是为标准的机器学习模型而设计的，其目标与隆升模型重要。为了解决这个问题，我们介绍了一套针对提升建模的特征选择方法，从统计和信息理论中汲取灵感。我们对公开可用数据集的拟议方法进行了经验评估，证明了与传统特征选择相比，所提出的方法的优势。我们将建议的方法公开作为Causalml开源软件包的一部分。
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is importantly different from uplift models. To address this, we introduce a set of feature selection methods explicitly designed for uplift modeling, drawing inspiration from statistics and information theory. We conduct empirical evaluations on the proposed methods on publicly available datasets, demonstrating the advantages of the proposed methods compared to traditional feature selection. We make the proposed methods publicly available as a part of the CausalML open-source package.
