Advanced Quantitative Methods: Causal Inference for Political Science
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, using the potential outcomes framework of causality, 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, including experiments (laboratory, field, and natural); matching; regression; weighting; fixed-effects; difference-in-differences; regression discontinuity designs; instrumental variables; and synthetic control. Examples are drawn from many areas of political science, including political behaviour, institutions, international relations, and public administration.
This is an advanced course intended for students who have already had some training in quantitative methods for data analysis (to the level of PUBL0055 or equivalent). If you are unsure whether your prior statistical training is sufficient to take this module, please contact Jack. If you do not have the required prerequisites but still wish to take the module, you should work through any good textbook for introductory level statistics (for example, Alan Agresti (2017), Statistical Methods for the Social Sciences, Pearson). However, you should note that this course is demanding and it will take a lot of work to keep up without the required background.
The goal of the course is to teach students to understand and apply various statistical methods and research designs for answering causal questions in the social sciences. In addition to the theoretical material, students will also learn data analytic skills using the statistical software package R. More information can be found in the syllabus.
Please note that this module is only available to SPP students. Students taking this course are not permitted to take PUBL0055A/B Introduction to Quantitative Methods.
The main textbooks we will use on the course are:
The Angrist and Pischke book is generally known as ``Mostly Harmless’’ or MHE, and this will be the primary text book. The Gerber and Green book provides an excellent introduction to the potential outcomes framework, as well as being very useful for the weeks on randomized experiments and instrumental variables.
For students who struggle with the content in MHE at first pass, you may also wish to consult the following:
- Angrist and Pischke (2015), Mastering `Metrics: The Path from Cause to Effect, Princeton University Press.
This book covers much of the same material as MHE, though with slightly more intuition, and slightly less maths. Throughtout the reading list, I have indicated the relevant chapters from each book. I would recommend that you read chapters from both books wherever possible.
It is worth noting that the materials for this course
shamelessly plagiarise draw inspiration from lecture notes and problem sets by Dominik Hangartner, Andy Eggers, Danny Hidalgo, and Teppei Yamamoto (and probably others).
Last Updated: 4 Dec 2018 2:15 pm GMT