Modern Methods in Statistics, Econometrics, and Machine Learning

Spring 2023

This course provides a brief introduction to a variety of topics in modern statistics, econometrics, and machine learning. Exact topics are to be determined, but may include: neural networks, random forests, text analysis, network analysis, empirical processes, multiple testing, randomization inference, neyman orthogonality, and shape restrictions. Guest lecturers include:

• Whitney Newey, MIT
• Daniel Wilhelm, Ludwig-Maximilians-Universitat Munchen
• Tim Armstrong, USC
• Joe Romano, Stanford University
• Stefan Wager, Stanford University
• Elena Manresa, NYU
• Xiaohong Chen, Yale University
• Andres Santos, UCLA
• Frank Wolak, Stanford