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Causal Inference

Course Dates and Times

Date: Monday 5 – Friday 9 February 2024
Duration: 3 hours of live teaching per day
Time: 16:00 – 19:00 CET

Vicente Valentim

This course offers an immersive online learning environment that employs state-of-the-art pedagogical tools. With a maximum of 16 participants, our teaching team can provide personalised attention to each individual, catering to their specific needs. The course is designed for a demanding audience, including researchers, professional analysts, and advanced students.

Purpose of the course

This introductory course on causal inference techniques will teach you state-of-the-art tools for establishing causal relations in the social sciences. Emphasising intuition, the course will equip you to deepen your knowledge of these methods independently and engage with the methodological debate surrounding them. You will learn popular methods of causal inference in the social sciences and how to apply them to research topics of interest, giving you a strong foundation for further study.

ECTS Credits

4 credits - Engage fully in class activities and complete a post-class assignment

Instructor Bio

Vicente Valentim is Postdoctoral Prize Research Fellow at Nuffield College, University of Oxford.

He earned his PhD from the European University Institute in 2021. Vicente studies how democracies generate norms against behaviour associated with authoritarianism, how those norms are sustained, and how they erode. He also has a keen interest in political methodology–especially causal inference methods.

Vicente’s work has been published or accepted in journals like the Journal of Politics, British Journal of Political Science, or Comparative Political Studies. It has been awarded the GESIS Klingemann Prize for best CSES Scholarship and the EUI Linz-Rokkan Prize for best thesis in Political Sociology. Read more about Vicente and his work.


Key topics covered

Day 1

To start, we will delve into the challenges of establishing causality in the social sciences. You will gain an understanding of the fundamental problem of causal inference and the potential outcomes framework, which provide a solid foundation for making causal claims in research. Through detailed discussions and examples, you will also learn about experiments — how they work, why they are important, and how they can help establish causal relations in social science research.

Day 2

You will learn about instrumental variables (IVs), discussing their assumptions, how to estimate them, and replicate previous work that uses this method. You will also briefly discuss ideas on how to find good IVs.

Day 3

Learn about difference-in-differences (DIDs), discussing their assumptions, how to estimate them, and replicate previous work that uses this method. We will also briefly discuss the recent explosion of literature using this topic and some thoughts on how to navigate it.

Day 4

You have the opportuntity to explore regression discontinuity designs (RDDs), discussing their assumptions, and how to estimate them. You will replicate previous work that uses this method and discuss fuzzy RDDs and how they connect to instrumental variables.

Day 5

You will learn about causal inference as a way of thinking. Good causal work is not just grounded in solid knowledge of estimation. It requires the ability to think of potential pitfalls of different designs, and to be ingenious in finding identification strategies. You will learn some techniques that make this easier, and we'll discuss how to apply the methods learned over the week to the topics in which each participant is interested.

How the course will work online

The course is structured into five live Zoom sessions, each lasting 3 hours each day. The course provides a safe and collaborative environment for discussing students' work and published research. Our ultimate goal is to make the course useful, and the final session will focus on specific topics of interest to each student.

Throughout the course, we will frequently discuss each student's research. You will be provided with methodological and applied readings to deepen your understanding of the methods we learn. We will also spend substantial time replicating previous work using R, which will give you the confidence to interpret software output for each method we discuss.

You should have a good understanding of linear regression, including interactions, some understanding of panel data, and some knowledge of R.