MACHINE LEARNING FOR ECONOMISTS
Introduction to the use of Machine Learning (ML) in economic analysis. Covers basic techniques of ML, much attention will be devoted to evaluating the use of these tools in economics. Learn how economists are integrating the tools of ML with econometric techniques in current empirical research. Gain hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application.
Topics
- (i) an in-depth discussion of the differences and similarities in goals
- empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification
- unsupervised learning methods
- large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics
- designed for causal inference
- optimal policy estimation
- estimation of counterfactual effects
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: Declared in an Economics graduate program Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Summarize the basic concepts of machine learning from an economist's perspective, and how these tools complement the econometrics toolkit Audience: Graduate 2. Classify differences and similarities between econometrics and machine learning Audience: Graduate 3. Implement new methods at the intersection of machine learning and econometrics that can be used to answer economic questions Audience: Graduate 4. Apply machine learning to datasets in economics Audience: Graduate 5. Develop and enhance analytic skills Audience: Graduate 6. Write functional code in R Audience: Graduate Learning Outcomes: 1. Summarize the basic concepts of machine learning from an economist's perspective, and how these tools complement the econometrics toolkit Audience: Graduate 2. Classify differences and similarities between econometrics and machine learning Audience: Graduate 3. Implement new methods at the intersection of machine learning and econometrics that can be used to answer economic questions Audience: Graduate 4. Apply machine learning to datasets in economics Audience: Graduate 5. Develop and enhance analytic skills Audience: Graduate 6. Write functional code in R Audience: Graduate
Sources
- Course catalogue ↗2026-2027