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.
翻译: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特征在多大程度上影响 {it 有条件平均治疗效果} (CATE) 的程度来寻找这些特征。 但是,这些方法可能会忽略重要特征,因为衡量平均治疗效果的CATE无法检测出平均值以外的分布参数差异(例如差异)。 为了解决现有方法的这一弱点,我们提议了一个特征选择框架,用于发现 ~ 分配处理效果修正者}。 我们首先制定特征重要性衡量标准,以量化特征属性对潜在结果分布差异影响的程度。 然后我们得出其计算效率高的估量器,并开发一种特征选择算法,以控制I型误差率达到预期水平。 实验结果表明,我们的框架成功发现了重要特征,并超越了现有平均方法。