In this Ph.D.-level course, we will discuss statistical methodology for use in causal inference in econometric settings. The course will cover topics such as basic treatment effect models (the linear model, linear IV model, linear panel data models), an introduction to nonparametric estimation, and estimation of treatment effects when effects may be heterogeneous. It is assumed that students are familiar with linear regression and methods of performing classical inference using asymptotic approximations for linear and nonlinear models.
Business 41901 and 41902.
Canvas. Journal articles and book chapters will be used in this course. A few references that may be useful are Hayashi Econometrics, Wooldridge Econometric Analysis of Cross Section and Panel Data, Angrist and Pischke Mostly Harmless Econometrics, Angrist and Pischke Mastering Metics, and Imbens and Rubin Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction.
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: June 17 2025
