Causal Inference with Cross-Sectional Data

Economists for Ukraine Workshop Fundraiser

Instructor: Jeffrey M. Wooldridge (Michigan State University)

Dates: December 7-8, 2023

Time: 9:00 am to 3:30 pm, EST
 
Location: online
 
Registration information: the registration fee is $200 for non-students, $100 for students, made in the form of a donation to Economists for Ukraine’s humanitarian aid fund. Register here
 
People currently living in middle- or low-income countries, as defined here, are eligible for a 50% and 75% discount, respectively. (Note: if you are from a low- or middle-income country but are currently living in a high-income country, you are NOT eligible for a discount.) Ukrainians who were residing in Ukraine as of February 23, 2022 can attend for free.
 

Description: This course covers the potential outcomes approach to identification and estimation of causal (or treatment) effects in several situations that arise and various empirical fields. The settings include unconfounded treatment assignment (with randomized assignment as a special case), confounded assignment with instrumental variables, and regression discontinuity designs. We will cover doubly robust estimators assuming unconfoundedness and discuss covariate balancing estimators of propensity scores. Local average treatment effects, and some recent results on including covariates in LATE estimation, also will be treated. Regression discontinuity methods, both sharp and fuzzy designs, and with control variables, round out the course.

Participants should have good working knowledge of ordinary least squares estimation and basic nonlinear models such as logit, probit, and exponential conditional means. Sufficient background is provided by my introductory econometrics book, Introductory Econometrics: A Modern Approach, 7e, Cengage, 2020. My book Econometric Analysis of Cross Section and Panel Data, 2e, MIT Press, 2010, covers some material at a higher level. I will provide readings for some of the more advanced material. While the focus here is on cross-sectional data, many of the methods have been applied to panel data settings, particularly to difference-in-differences designs. Course material, including slides and Stata files, will be made available via Dropbox.

 

The lectures and the Q&A sessions will be recorded and the recordings will be made available to participants.

 

Course Timetable (Times are EST)

December 7, 2023
9:00-10:30    Session 1: Introduction; Potential Outcomes and Parameters; Unconfoundedness and Overlap; Identification of Average Treatment Effects

10:30-11:00    Q&A/Break

11:00-12:30    Session 2: Propensity Score Estimation; Evaluating and Improving Overlap; Regression Adjustment; Inverse Probability Weighting

12:30-13:30    Q&A/Break

13:30-15:00    Session 3: Doubly Robust Estimators; Matching Estimators; Other Sampling and Assignment Schemes
 
15:00-15:30    Q&A/Summary
 
December 8, 2023

9:00-10:30    Session 4: Potential Outcomes and the Local Average Treatment Effect; LATE with Covariates; Doubly Robust Estimators of LATE

10:30-11:00    Q&A/Break
 
11:00-12:30    Session 5: Heterogeneity and IV Methods; Control Function Methods and Switching Regression; Nonlinear Response Models

12:30-13:30    Q&A/Break

13:30-15:00    Session 6: Sharp Regression Discontinuity Designs; Fuzzy RD; Including Covariates in RD

15:00-15:30    Q&A/Summary