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This course provides an introduction to statistical methods used for causal inference in the social sciences. We will be concerned with understanding how and when it is possible to make causal claims in empirical research. In particular, we will focus on understanding which assumptions are necessary for giving research a causal interpretation, and on learning a range of approaches that can be used to establish causality empirically. The course will be practical – in that you can expect to learn how to apply a suite of methods in your own research – and theoretical – in that you can expect to think hard about what it means to make claims of causality in the social sciences.

We will address a variety of topics that are popular in the current political science literature. Topics may include experiments (laboratory, field, and natural); matching; regression; weighting; fixed-effects; difference-in-differences; regression discontinuity designs; instrumental variables; and synthetic control. Examples are typically drawn from many areas of political science, including political behaviour, institutions, international relations, and public administration.

The goal of the module is to teach students to understand and confidently apply various statistical methods and research designs that are essential for modern day data analysis. Students will also learn data analytic skills using the statistical software package R.

This is an advanced module intended for students who have already had some training in quantitative methods for data analysis. One previous course in quantitative methods, statistics, or econometrics is required for all students participating on this course. Students should therefore have a working knowledge of the methods covered in typical introductory quantitative methods courses (i.e. at least to the level of PUBL0055 or equivalent). At a minimum, this should include experience with hypothesis testing and multiple linear regression.