About the Course
Many causal inference problems involve the notion of time: Do states that expanded Medicaid in a given year have better mortality rates than states that have not yet expanded Medicaid? How does the implementation of a household heat pump affect energy consumption in the months after the change? How does a new medical treatment affect the quality of life of patients in the years to come?
To answer causal questions like these, it is common to explore data from multiple units across different points in time, such as longitudinal and panel data. In recent years, such datasets have become available at incredible levels of detail about the units of interest. This course introduces Modern Causal Panel and Longitudinal Data techniques and how to apply them.
Topics Covered
Potential Outcomes
Causal framework for panel data, treatment sequences, causal parameters, parallel trends
Experimental Designs
Randomizing treatment sequences, baseline vs. sequential randomization, staggered rollout
Difference-in-Differences
Basic DiD, multiple time periods, staggered adoption, heterogeneous treatment effects
Event Studies
Pre-trends testing, sensitivity analysis, credible inference under violations
Synthetic Controls
Classic SC, augmented SC, matrix completion, synthetic difference-in-differences
Other Topics
Selection based on lagged outcomes, triple differences, continuous treatments, instrumented DiD, treatment turning on and off, long-term effects via surrogates
Instructor
Pedro H.C. Sant'Anna
Email: pedro.santanna@emory.edu
Office Hours: Tuesdays 1:00-2:00 PM
Website: psantanna.com
Teaching Assistant
Name: Marcelo Ortiz-Villavicencio
Key Resources
Recommended Surveys
- Roth, Jonathan, Pedro H.C. Sant'Anna, Alyssa Bilinski, and John Poe (2023), "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature," Journal of Econometrics, 235(2), 2218-2244. doi:10.1016/j.jeconom.2023.03.008
- Baker, Andrew C., Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H.C. Sant'Anna (2025), "Difference-in-Differences Designs: A Practitioner's Guide," Journal of Economic Literature, Forthcoming. doi:10.1257/jel.20251650
- Abadie, Alberto (2021), "Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects," Journal of Economic Literature, 59(2), 391-425. doi:10.1257/jel.20191450
- Arkhangelsky, Dmitry and Guido W. Imbens (2024), "Causal Models for Longitudinal and Panel Data: A Survey," The Econometrics Journal, 27(3), C1-C61. doi:10.1093/ectj/utae008
- Callaway, Brantly (2023), "Difference-in-Differences for Policy Evaluation," in Klaus F. Zimmermann, ed., Handbook of Labor, Human Resources and Population Economics, Cham: Springer International Publishing, pp. 1-61. doi:10.1007/978-3-319-57365-6_352-1