Advanced Quantitative Methods (elective)

The course provides a broad overview of quantitative methods as they are commonly used in political science. Its primary focus is on methods for causal inference (experiments, instrumental variables, difference-in-differences, and regression discontinuity), and their application in the statistical software R.

The course will examine some of the best-known and recent work in quantitative political science, and ask: How did the authors produce their results? Can we re-produce them? How plausible are the assumptions for causal inference? And how sensitive are the results to different methodological choices? The course will place particular emphasis on students' ability to apply the methods introduced to real-world data from top-level political science articles.

The course revolves around two sections:

1. Short introduction to R and linear regression

The first few lectures of the course will re-introduce you to R and linear regression analysis (OLS), and introduce you to LaTeX, a type-setting system used by the majority of researchers who do advanced quantitative research in political science. LaTeX should be very useful to you when you write your assignments and, later, your Master’s thesis.

For students who do not yet know R, I will send out an introduction to R and online labs to complete before the class starts.

2. Applied causal inference

The second section of the course will focus on causal inference, and recent methods developed for estimating causal effects. The social sciences (particularly economics and political science) underwent a "credibility revolution" in the late 2000s, which places methods for estimating causal effects at the center of research designs. This is because theoretically driven empirical research often centers on causal questions. As a result, much of the course will emphasize how to think about causality, and the statistical techniques for causal inference.