Estimating Causal Effects with Panel Data, 2,5 credits
The course Estimating Causal Effects with Panel Data is a third-cycle course that provides an advanced, applied introduction to modern methods for causal inference using panel data. The course is aimed at doctoral students and researchers in education and related social sciences who wish to deepen their understanding of causal identification strategies in longitudinal and register-based data.
The course introduces key theoretical and practical tools for estimating causal effects in panel-data settings, including Difference-in-Differences, event-study designs, fixed-effects models, and matching-based estimators. Particular attention is given to identification assumptions, diagnostics, and the interpretation of treatment effects under different data structures. Throughout the course, participants engage with empirical applications and reproduce published analyses using statistical software, with a strong emphasis on hands-on implementation, critical assessment of assumptions, and evaluation of causal claims in applied research.
The course combines lectures with follow-up sessions focused on practical exercises, allowing participants to actively work with data while reflecting on how different methodological choices affect inference in their own research contexts. The course is part of the R-QRM research school and supports participants in developing advanced quantitative skills for register-based and longitudinal research in education.
Entry requirements
For admission, the applicant must be registered as a doctoral student (third-cycle) or hold a doctoral degree. The course presumes prior knowledge of basic statistics, including regression analysis and statistical inference, as well as familiarity with the concept of causal inference. Experience with R (or equivalent statistical software) is recommended.
Practical information
Course code
RQRM251
Application period
19 December 2025 – 1 March 2026