Short Bio
I am Associate Professor of Comparative Politics and the Politics Tutorial Fellow at Brasenose College. My current research focuses on the impact of political socialization on the formation of partisan identities and on the ideological legacies of authoritarian regimes. I teach a variety of courses in research methods, statistics and causal inference. I obtained my PhD from the European University Institute at Florence. I have held previous positions as lecturer at the University of Nottingham and as Prize Postdoctoral Research Fellow at Nuffield College at Oxford.
Prerequisite knowledge
Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration.
Participants are expected to be familiar with the OLS regression estimator. Although the notions of unconditional and conditional expectation will be introduced in the first session, prior exposure to these terms would be helpful in fully grasping the potential outcomes notation. We will be working mainly in Stata, but also in R. Full code will be provided. No perquisite knowledge of any specific software is requested.
Short course outline
The course will introduce participants to an authoritative framework of causal inference in social sciences. The objective is to learn how statistical methods can help us draw causal claims about phenomena of interest. By the end of the course, participants will be in position to 1) critically evaluate statements about causal relationships based on some analysis of data; 2) apply a variety of design-based easy-to-implement methods that will help them draw causal inferences in their own research.
One of the keys goals of empirical research is to test causal hypotheses. This task is notoriously difficult without the luxury of experimental data. This course will introduce you into methods that allow you to make convincing causal claims without working with experimental data. By the end of the course, you will know how to estimate causal effects using the following designs:
1. Instrumental Variables
2. Regression Discontinuity Design
3. Difference-in-Differences
You can only learn statistics by doing statistics. This is why this course includes a laboratory component, where you will learn to apply these techniques to the analysis of discipline specific data.
@EliasDinas