ECON 4450

Machine Learning in Economics

Lec 01 selected

This course bridges machine learning (ML) techniques and causal inference methods to address primarily economic questions. It explores key ML concepts (penalized regression, regularization, cross-validation, and hyperparameter tuning), along with tree-based methods, and study their implementation in economic research. R-based labs allow students to combine ML with standard econometric tools for robust causal analysis and informed decision-making in economic applications.

Sections

Lecture

From the syllabus

From the Winter 2027 syllabus by Mutlu Yuksel. Open the PDF

Grading

100%Assignments (5)20% each

Textbook

  • Causal Inference and Machine Learning in Economics, Social, and Health Sciences, Mutlu Yuksel and Yigit Aydede (optional, free)

Course rules

Late work
Assignments and review papers are accepted up to three days late with a 10% penalty per day.
Late work
After three days, work is not accepted without prior instructor approval.
Missed work
With instructor approval, a missed assignment may be replaced by a replication study.
Collaboration
Students may work in pairs on assignments, but each must disclose contributions and submit an individual write-up.
Sources
All sources, including AI tools, must be explicitly cited.

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