This course presents a modern approach to studying causal questions in political
science. In the first half of the course we introduce the core concepts of causality
and inference, how to do experiments and two basic frameworks for analyzing
data (linear regression and matching).
In the second part of the course we
look at different strategies for identifying causal effects using observational data.
Throughout, we will look at different applications of the methods we use, and we
will also give you a chance to apply the different methods in weekly assignments.
The overall goal of the course is to become a critical consumer of causal
claims in the social sciences and to give you the tools needed to do causal
inference in practice.
In the first class you will be split into study groups. You can use these study groups to complete the weekly assignments which are uploaded to Blackboard one week before each class.
In class, we will use Stata 16 and, in some cases, the statistical programming language R. License to Stata can be bought online via the department's website.
R studio can be downloaded for free online. However, you are free to use whatever software you want.
We will use longer excerpts from the following books
If you have an older version of Stock and Watson feel free to use that. We
recommend that you buy Mostly Harmless and the Field Experiments book. The
latter cannot be bought from PB, but can be purchased online. The chapters
you will have to read from Field Experiments also available as pre-prints, and
can be found if you are good at Google.
Read Appendices A and B for good context on some of the notation and operators that we will use.