  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.
