ECO 4370 / 6370 Computing in Economics

Fall 2026

Instructor: Zhan Gao
Course information: See Syllabus.

Part of the course content related to programming and data science tools is adapted from Grant McDermott's Data Science for Economists course and Zhentao Shi's course with the same title.
Some portions of the course content related to machine learning and causal inference are built upon lecture notes developed by Michael Leung, Tim Armstrong and Roger Moon at USC based on the textbook Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions by Matt Taddy.
Some portions of the course content related to regression models and regularized regression leverage on the lecture notes developed by Jacob Bien at USC for the course DSO 699.
Other key references

R for Data Science by Hadley Wickham and Garrett Grolemund
Advanced R by Hadley Wickham
An Introduction to Statistical Learning (with applications in R) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Mostly Harmless Econometrics by Joshua D. Angrist and Jörn-Steffen Pischke
Applied Causal Inference Powered by ML and AI by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis

Python for Data Analysis by Wes McKinney
Coding for Economists by Arthur Turrell
An Introduction to Statistical Learning (with applications in Python) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor