Feature selection is an extensively studied technique in the machine learning literature where the main objective is to identify the subset of features that provides the highest predictive power. However, in causal inference, our goal is to identify the set of variables that are associated with both the treatment variable and outcome (i.e., the confounders). While controlling for the confounding variables helps us to achieve an unbiased estimate of causal effect, recent research shows that controlling for purely outcome predictors along with the confounders can reduce the variance of the estimate. In this paper, we propose an Outcome Adaptive Elastic-Net (OAENet) method specifically designed for causal inference to select the confounders and outcome predictors for inclusion in the propensity score model or in the matching mechanism. OAENet provides two major advantages over existing methods: it performs superiorly on correlated data, and it can be applied to any matching method and any estimates. In addition, OAENet is computationally efficient compared to state-of-the-art methods.
翻译:在机器学习文献中,选择地物是一种广泛研究的技术,其主要目的是确定提供最高预测力的特征的子集。然而,在因果推断中,我们的目标是确定与治疗变量和结果(即混乱者)相关的一系列变量。虽然控制混杂变量有助于我们实现对因果关系的不偏袒估计,但最近的研究表明,控制纯结果预测器和混结者可以减少估计值的差异。在本文中,我们提议了一种结果适应性埃力-网络(OAENet)方法,专门设计该方法是为了进行因果推断,以选择组合体和结果预测器,将其纳入偏向性评分模型或匹配机制。OAENet比现有方法有两大优势:它优于相关数据,它可以应用于任何匹配方法和任何估计。此外,OAENet与最新方法相比,计算效率很高。