论文标题
基于双机器学习的计划评估
Double Machine Learning based Program Evaluation under Unconfoundedness
论文作者
论文摘要
本文回顾，应用和扩展了基于双机器学习（DML）的最近提出的方法，重点是在不满足的情况下进行程序评估。基于DML的方法利用灵活的预测模型来调整（i）标准平均效应，（ii）不同形式的异质效应和（iii）最佳治疗分配规则的混杂变量。对瑞士活跃劳动力市场政策的多个计划的评估说明了基于DML的方法如何实现全面的计划评估。由DR-Learner的极端个性化治疗效果估计的动机，我们提出了归一化的DR-Learner（NDR-Learner）来解决此问题。 NDR-Learner承认，个性化效应估计值可以通过对反比概率权重的个性化归一化来稳定。
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. An evaluation of multiple programs of the Swiss Active Labour Market Policy illustrates how DML based methods enable a comprehensive program evaluation. Motivated by extreme individualised treatment effect estimates of the DR-learner, we propose the normalised DR-learner (NDR-learner) to address this issue. The NDR-learner acknowledges that individualised effect estimates can be stabilised by an individualised normalisation of inverse probability weights.
