Taught by me, Kirill Borusyak, at UC Berkeley's Department of Agricultural and Resource Economics, Fall 2023

Please email me at [email protected] if you notice typos, mistakes, or have other suggestions for how to improve the course.

Course outline:

A. Introduction: regression and causality

• A1. Key facts about regression

• A2. Potential outcomes and randomized control trials

B. Selection on observables

• B1. Regression adjustment

• B2. Matching and propensity score methods

• B3. Doubly-robust methods

C. Panel data methods

• C1. Linear panel data methods recap

• C2. Canonical difference-in-differences (DiD) and event studies

• C3. DiD with staggered adoption

• C4. Synthetic control methods and factor models

D. Instrumental variables (IVs)

• D1. IV idea and mechanics. Weak instruments

• D2. IV with heterogeneous effects

• D3. Shift-share IV and formula instruments

• D4. Examiner designs (“judge IVs”). (+A bit on control functions)

E. Regression discontinuity (RD) designs

• E1. Sharp RD designs

• E2. RD extensions: fuzzy RD, spatial RD, RD extrapolation, and more

F. Miscellaneous topics: Models with multiplicative effects and Poisson regression; More on statistical inference

--

Materials are distributed under the Creative Commons Attribution 4.0 license.