Double Machine Learning (DML) is a powerful framework that combines the flexibility of machine learning with the robustness of statistical inference. It is particularly useful in settings where treatment effects are of interest, such as in econometrics and causal inference. In this article, we explore an object-oriented approach to implementing Double Machine Learning using Python, leveraging the simplicity and modularity of object-oriented programming (OOP) principles.