This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance functions arise naturally in many settings, such as when controlling for confounding variables or leveraging instruments. The paper describes two biases that arise from nuisance function estimation and explains how DML alleviates these biases. Consequently, DML allows the use of flexible methods, including machine learning tools, for estimating nuisance functions, reducing the dependence on auxiliary functional form assumptions and enabling the use of complex non-tabular data, such as text or images. We illustrate the application of DML through simulations and empirical examples. We conclude with a discussion of recommended practices. A companion website includes additional examples with code and references to other resources.

翻译：本文对双重/去偏机器学习（DML）方法进行了介绍。DML是一种在存在干扰函数（即识别目标参数所需但并非主要关注对象的函数）时进行目标参数推断的通用方法。干扰函数在许多场景中自然出现，例如在控制混杂变量或利用工具变量时。本文描述了由干扰函数估计引起的两种偏差，并解释了DML如何缓解这些偏差。因此，DML允许使用包括机器学习工具在内的灵活方法来估计干扰函数，从而减少对辅助函数形式假设的依赖，并支持使用复杂的非表格数据（如文本或图像）。我们通过模拟和实证案例说明了DML的应用。最后，我们对推荐实践进行了讨论。配套网站提供了包含代码的额外示例以及其他资源的参考文献。