In this Ph.D.-level course, we will discuss statistical methodology with a focus on methods used for causal inference. The course will review basic models commonly used in social science research (the linear regression model and linear IV model) and cover additional topics such as estimation of treatment effects when effects may be heterogeneous, workhorse panel data models, estimation using flexible/high-dimensional methods, and group-based inference for dependent data. It is assumed that students have a good understanding of linear regression and methods of performing classical inference using asymptotic approximations for linear and nonlinear models.
Business 41901 and 41902.
Canvas. There is no required text for this course. Some recommended texts are Econometrics by Hayashi, Econometric Analysis of Cross Section and Panel Data by Wooldridge, Mostly Harmless Econometrics by Angrist and Pischke, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Imbens and Rubin, and An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani.
Based on a project, final, and problem sets.
- Allow Provisional Grades (For joint degree and non-Booth students only)
Description and/or course criteria last updated: February 09 2026
