This course introduces students to analyzing and employing machine learning algorithms to evaluate public policies. To that end, the student first becomes conversant with the core issues of causal statistics, such as the potential outcomes framework, drawing causal diagrams, and recognizing sufficient conditions for statistical identification. Simultaneously, the class touches on the building blocks of R, including data wrangling and functional programming. After acquiring basic knowledge of coding and causal statistics, the material gravitates around the building blocks of machine learning (ML) and their implementation in R. Subsequently, the student learns about the meaningful overlaps between causal statistics and ML by reviewing the notions of Causal Trees and Causal Forests. Finally, a significant portion of the course addresses a series of applications concerning evaluations of public initiatives, such as police reforms, environmental preservation, and educational programs.