We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learning (DML) and uses efficient machine learning instruments (MLIV) and kernel smoothing. We prove consistency and asymptotic normality of our estimator and also construct confidence sets that are more robust towards weak IV. Along the way, we also provide an accessible discussion of the corresponding estimator for the homogeneous treatment effect with efficient machine learning instruments. The methods are evaluated on synthetic and real datasets and an implementation is made available in the R package IVDML.

翻译：本文提出一种新的工具变量（IV）估计方法，用于存在内生性情况下的异质性处理效应估计。该估计器基于双重/去偏机器学习（DML）框架，结合高效机器学习工具变量（MLIV）与核平滑技术。我们证明了估计量的一致性与渐近正态性，并构建了对弱工具变量更具鲁棒性的置信集。此外，本文还对采用高效机器学习工具变量的同质性处理效应估计器进行了通俗易懂的讨论。方法在合成数据与真实数据集上进行了评估，相关实现已发布于R软件包IVDML中。