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ECON 726

APPLICATIONS OF MACHINE LEARNING IN ECONOMICS

3 credits.

Techniques of Machine Learning (ML) and the use of these tools in economics. Exploration of how economists are integrating ML tools with econometric techniques in current empirical research. 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

Topics explicitly listed in official catalogue description

Prerequisites & course information

Requisites: ECON 725 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 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. Apply machine learning methods to real economic data Audience: Graduate 3. Evaluate the advantages and disadvantages of alternative machine learning algorithms in economic applications 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. Apply machine learning methods to real economic data Audience: Graduate 3. Evaluate the advantages and disadvantages of alternative machine learning algorithms in economic applications Audience: Graduate

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