The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.

翻译：R包DoubleML实现了Chernozhukov等人（2018）提出的双重/去偏机器学习框架。该框架基于机器学习方法，提供因果模型参数的估计功能。双重机器学习框架包含三个关键要素：Neyman正交性、高质量机器学习估计及样本分割。干扰成分的估计可通过mlr3生态系统中提供的多种前沿机器学习方法实现。DoubleML支持在多种因果模型中进行推断，包括部分线性回归模型、交互回归模型及其在工具变量估计中的扩展。DoubleML的面向对象实现为模型设定提供了高度灵活性，并使其易于扩展。本文作为双重机器学习框架及R包DoubleML的导引，通过基于模拟和真实数据集的可复现代码示例，展示DoubleML用户如何借助机器学习方法进行有效推断。