基于树的方法中的核平衡

中文摘要 AI 辅助

研究异质性处理效应在实验和观察性研究中已变得至关重要。获得可靠处理效应估计的一个关键假设是重叠性，该假设要求处理组和对照组单位具有足够相似的协变量分布。重叠性差可能会限制估计器的有效性，尤其是基于倾向得分的估计器，从而可能导致不可靠的结果。我们研究了核平衡（KBal）(Hazlett, 2020) 作为条件平均处理效应（CATE）估计中倾向得分方法替代方案的有效性，特别是在存在重叠性违反的情况下。基于优化平衡方法，我们将KBal权重整合到基于树的方法中，具体包括因果森林（Athey等人，2019）和X学习器（XRF）(Künzel等人，2019)，以评估它们对偏差减少和估计精度的影响。蒙特卡洛证据表明，KBal在变换后的特征空间中实现了近乎精确的平衡，从而在传统重加权方法因极端权重、有限样本偏差或未能充分消除既有混杂偏差而失效的情况下，改善了处理效应估计。我们将所提出的方法应用于半合成IHDP基准数据集。总体而言，结果表明KBal带来了性能提升，特别是在非线性处理效应和有限重叠性的设置中，使其成为倾向得分方法的一个有用替代方案。

英文摘要

Studying heterogeneous treatment effects has become essential in experimental and observational studies. A critical assumption for obtaining reliable treatment effect estimates is overlap, which requires that treated and control units have sufficiently similar covariate distributions. Poor overlap may limit the effectiveness of estimators, especially those based on propensity scores, potentially leading to unreliable results. We investigate the effectiveness of kernel balancing (KBal) (Hazlett, 2020) as an alternative to propensity score methods for conditional average treatment effect (CATE) estimation, particularly in settings with overlap violations. Building on optimization-based balancing approaches, we integrate KBal weights into tree-based methods, specifically, causal forests (Athey et al., 2019) and the X-Learner (XRF) (Künzel et al., 2019), to assess their impact on bias reduction and estimation precision. Monte Carlo evidence shows that KBal achieves near-exact balance in a transformed feature space, thereby improving treatment effect estimation in cases where traditional reweighting methods struggle due to extreme weights, finite-sample bias, or insufficient removal of pre-existing confounding bias. We apply the proposed methods to the semi-synthetic IHDP benchmark dataset. Overall, the results indicate that KBal leads to performance improvements, especially in settings with nonlinear treatment effects and limited overlap, making it a useful alternative to propensity score methods.

发表机构

• University of Duisburg-Essen（杜伊斯堡-埃森大学）

机构由 AI 辅助整理，请以论文原文为准。