Experiments, Regression & Causal Analysis for Predictive Modeling and Policy Evaluation
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.
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.
Course Content
- Foundations of Linear Regression Analysis –> 11 lectures • 48min.
- Machine Learning for Prediction and Model Evaluation –> 13 lectures • 45min.
- Deep Dive into Linear Regression and Causal Inference –> 28 lectures • 2hr 4min.
- Experimental Design and Difference-in-Differences Analysis –> 10 lectures • 55min.
- Instrumental Variables and Advanced Causal Inference –> 19 lectures • 1hr 48min.
Requirements
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