Overview

This course provides a broad overview of quantitative methods for causal inference in the social sciences (experiments, instrumental variables, difference-in-differences, and regression discontinuity) and their application in the statistical software R. The course is split into two sequential sections:

1. Statistical programming in R

The first classes introduce the statistical programming language R, the most widely used statistical software among political scientists who use advanced quantitative methods. Like Python, R’s data science counterpart, R is also widely used in government and the private sector for data analysis. For applied academic, government, and private-sector research, knowing R (and Python) will likely be the most valuable technical tools that you learn from your time in university. Accordingly, you will use R in every class throughout the term. You will learn R in class, and through exercises on DataCamp, an intuitive platform for learning a variety of statistical programming languages and methods.

The first section of the course will also re-introduce linear regression analysis (OLS), which you should be familiar with from your undergraduate methods sequence.

Finally, this section will introduce you to LaTeX, a type-setting system used by the majority of researchers who do advanced quantitative research in the social and natural sciences. LaTeX should be useful to you when you write your assignments and, later, your Master’s thesis.

2. Causal inference

The second and biggest section of the course will focus on causal inference and methods developed for estimating causal effects with experimental and observational data. The social sciences 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 focuses on causal questions. Much of the course thus emphasizes how to think about causality, and the statistical techniques for causal inference.

Logistics

Classroom day and time

ROOM on DAY from HOUR-HOUR

Office hours & e-mail

Gregory Eady
E-mail: gregory.eady@ifs.ku.dk
Office: 18.2.10
Office hours: Fridays 13-15 (by scheduled meeting)

Schedule a meeting
Meeting times will appear 7 days before each Friday
i.e. you can’t book far into the future

Course materials

Two texts will be used in the course and can be purchased from Academic Books:

  1. Mastering ‘Metrics: The Path from Cause to Effect
    Princeton University Press, 2014.
    Joshua D. Angrist and Jörn-Steffen Pischke

  2. Field Experiments: Design, Analysis, and Interpretation
    W. W. Norton & Company, 2012.
    Alan S. Gerber and Donald P. Green.

The following book covers similar material and is a free online supplement to the two above. It is not a substitute for the two books above: all of the required readings are from “Mastering ‘Metrics” and “Field Experiments: Design, Analysis, and Interpretation”.

Portfolio Exam

The course is graded using a portfolio exam that will consist of mandatory assignments. The first assignment will be due mid-way through the course and is used to assess whether you are competent in the statistical software R. The second assignment is essentially a collection of assignments with suggested due dates throughout the term.