Applied Empirical Economics with R and Machine Learning

Experiments, Regression & Causal Analysis for Predictive Modeling and Policy Evaluation

What you'll learn
- Fundamentals of linear regression and ordinary least squares (OLS) estimation
- How to implement regression models in R
- Techniques to handle confounding variables and unobserved heterogeneity
- Predictive modeling using machine learning: regression trees, random forests, and cross-validation
- Deep dive into causal inference: endogeneity, instrumental variables, and treatment effects
- Design and analysis of controlled experiments and difference-in-differences (DiD)
- Application of instrumental variable estimation and inverse probability weighting
- Real-world case studies including job counseling experiments and search engine marketing

Requirements
- Basic understanding of statistics and data analysis
- Familiarity with R is helpful

Description
In today’s data-rich world, the ability to extract meaningful insights from economic data is more valuable than ever.Empirical Economics with Ris 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 interpretlinear regression modelsusing real data

Understand the mechanics and intuition behindordinary least squares (OLS)

Exploremachine learning techniqueslike regression trees and random forests for prediction

Dive intocausal inferenceusing tools like instrumental variables and difference-in-differences

Analyzecontrolled experimentsand quasi-experimental designs

Apply concepts toreal-world applicationsincluding marketing, education, and labor economics

Gain proficiency inR programmingfor statistical modeling and data visualization

Who this course is for:
- Data analysts and researchers seeking to strengthen their econometric and machine learning skills
- Economists and social scientists working on policy evaluation and impact assessment
- Business professionals interested in marketing analytics and experimental design
- Students and academics in economics, statistics, and data science
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