Doubly robust learning offers a robust framework for causal inference from observational data by integrating propensity score and outcome modeling. Despite its theoretical appeal, practical adoption remains limited due to perceived complexity and inaccessible software. This tutorial aims to demystify doubly robust methods and demonstrate their application using the EconML package. We provide an introduction to causal inference, discuss the principles of outcome modeling and propensity scores, and illustrate the doubly robust approach through simulated case studies. By simplifying the methodology and offering practical coding examples, we intend to make doubly robust learning accessible to researchers and practitioners in data science and statistics.

翻译：双重稳健学习通过整合倾向得分与结果建模，为观测数据的因果推断提供了一个稳健的框架。尽管其理论颇具吸引力，但由于方法看似复杂且缺乏易用软件，实际应用仍然有限。本教程旨在阐明双重稳健方法，并演示如何利用EconML包实现其应用。我们首先介绍因果推断的基本概念，讨论结果建模与倾向得分的原理，随后通过模拟案例研究阐述双重稳健方法。通过简化方法论并提供实用的代码示例，我们希望使数据科学与统计学领域的研究人员和从业者能够更便捷地掌握双重稳健学习。