Applied Empirical Economics with R and Machine Learning

In today’s data-rich world, the ability to extract meaningful insights from economic data is more valuable than ever. Empirical Economics with R is a comprehensive, hands-on course designed to equip learners with the tools and techniques needed to analyze real-world data, uncover causal relationships, and make informed decisions using statistical and machine learning methods.

This course takes you on a journey through the core pillars of empirical analysis—starting with foundational linear regression and progressing through advanced topics like causal inference, experimental design, and machine learning. You’ll learn not just how to run models, but how to interpret them, validate them, and apply them to real economic questions.

Whether you’re evaluating the impact of education on income, predicting wine quality, or assessing the effectiveness of job counseling programs, this course provides the analytical framework and coding skills to do so rigorously and confidently.

Through engaging lectures, practical coding exercises in R, and real-world case studies, you’ll gain a deep understanding of how economists use data to answer complex questions. You’ll also explore the limitations of models, the importance of assumptions, and the nuances of interpreting results in policy and business contexts.

Key Highlights

• Learn to build and interpret linear regression models using real data
• Understand the mechanics and intuition behind ordinary least squares (OLS)
• Explore machine learning techniques like regression trees and random forests for prediction
• Dive into causal inference using tools like instrumental variables and difference-in-differences
• Analyze controlled experiments and quasi-experimental designs
• Apply concepts to real-world applications including marketing, education, and labor economics
• Gain proficiency in R programming for statistical modeling and data visualization