Teaching
Machine Learning for Policy Analysis
Machine Learning for Policy Analysis is a graduate course on using machine learning methods in policy-relevant empirical work, with attention to discovery, measurement, prediction, designed and found data, privacy, fairness, interpretability, and translating models into practice.
Materials
2025 Lecture Slides
January 27/29
Week 1: Introduction
February 3/5
Week 2: Discovery and Measurement
February 10/12
Week 3: Prediction Within Population
February 17/19
Week 4: Prediction in a New Population
February 24/26
Week 5: Prediction to a Counterfactual Population
March 3/5
Week 6: Experimental Data
March 10/12
Spring Break
March 17/19
Week 7: Designed Data
March 24/26
Week 8: Found Data
March 31/April 2
Week 9: Privacy
April 7/9
Week 10: Fairness
April 14/16
Week 11: Interpretability
April 21/23