论文标题
发现分布处理效果修饰符的功能选择
Feature Selection for Discovering Distributional Treatment Effect Modifiers
论文作者
论文摘要
找到与治疗效果差异相关的特征对于揭示基本因果机制至关重要。现有方法通过测量特征属性影响{\ iT条件平均治疗效果}（CATE）的程度来寻求此类特征。但是，这些方法可能会忽略重要特征，因为CATE是平均治疗效果的度量，无法检测到平均值以外的其他分布参数差异（例如方差）。为了解决现有方法的这种弱点，我们提出了一个特征选择框架，以发现{\ IT分布处理效果修饰符}。我们首先制定了一个特征重要性度量，该测度量化了特征属性如何影响潜在结果分布之间的差异。然后，我们得出其计算有效的估计器，并开发了一种功能选择算法，该算法可以将I类误率控制为所需级别。实验结果表明，我们的框架成功地发现了重要特征，并胜过现有的基于均值的方法。
Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms. Existing methods seek such features by measuring how greatly the feature attributes affect the degree of the {\it conditional average treatment effect} (CATE). However, these methods may overlook important features because CATE, a measure of the average treatment effect, cannot detect differences in distribution parameters other than the mean (e.g., variance). To resolve this weakness of existing methods, we propose a feature selection framework for discovering {\it distributional treatment effect modifiers}. We first formulate a feature importance measure that quantifies how strongly the feature attributes influence the discrepancy between potential outcome distributions. Then we derive its computationally efficient estimator and develop a feature selection algorithm that can control the type I error rate to the desired level. Experimental results show that our framework successfully discovers important features and outperforms the existing mean-based method.
