In this course, students will learn the fundamentals of applied causal inference in political science using R. No prior knowledge of statistics or coding is required, as we start from the basics of R programming. The course aims to provide students with the essential quantitative analysis skills that are increasingly important in political science, particularly for their future research and Bachelor theses.

Throughout the course, we will work with real-world datasets, focusing on how to estimate causal relationships from observational (or "happenstance") data. Topics include natural experiments, matching, regression, difference-in-differences, panel methods, instrumental variable estimation and regression discontinuity designs. While we will introduce concepts such as regression and statistical inference, the primary focus is on the practical application of these methods to address important research questions. This course is designed to be applied rather than theoretical, helping students understand how to use data analysis techniques in political science. By the end of the course, students will be equipped with the tools and skills to conduct their own data analysis.

Imai, K., & Bougher, L. D. (2021). Quantitative social science: An introduction in Stata. Princeton University Press.
Llaudet, E., & Imai, K. (2023). Data analysis for social science: A friendly and practical introduction. Princeton University Press.

6CP: In the final session of the course, students will complete an independent data analysis task in class. This will be similar to a take-home exam, but conducted in a supervised in-class setting. Student performance will be assessed based on the quality and accuracy of their work on this task.