Menu
Menu
Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Member rate 2,713.79 zł
Non-Member rate 5,427.58 zł
Save 221.03 zł Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2025 or 2026, you qualify for 221.03 zł off your course fee.
Jagiellonian University: 8 – 11 September
Online: 14 – 15 September, 13:00 – 16:30 CEST
This course includes FREE observer access to the General Conference 2026!
Registration fees are listed in local Polish złoty (PLN). Please refer to Fees and Offers for the Sterling pound (GBP) conversion.
Many social science research projects aim to establish whether two events are causally related, i.e. whether X causes Y. Yet unlike natural scientists, social scientists cannot resort to randomised experiments. This course provides statistical tools to draw credible causal conclusions from observational data – the kind of messy, real-world data most researchers actually work with.
The course opens with the potential outcomes framework, which provides a rigorous foundation for thinking about causality. From there, you will learn about a range of research designs and methods – including matching, instrumental variables, difference-in-differences, and regression discontinuity – examining the assumptions each requires and the questions each is best suited to answer.
Throughout, examples are drawn from across the social sciences. You are encouraged to bring your own research questions to the table, working individually or in small groups to explore how these methods might apply to your work. By the end of the course, you will be able to critically evaluate causal claims in the literature and independently apply key research designs in your own project.
Gloria Gennaro is Assistant Professor in Public Policy and Data Science in the UCL Department of Political Science. Her research focuses on the political economy of advanced democracies in Western Europe and in the U.S. using causal inference techniques, machine learning and natural language processing. She has published work on the drivers and strategy of right-wing populism and anti-immigrant sentiment, and on remedies against online hate speech.
She has extensive experience teaching quantitative research methods, including causal inference and statistical learning.
This course introduces you to the main design-based approaches for drawing causal conclusions from observational data, covering the methods most commonly used across the social sciences, and in political science in particular.
The focus is on developing a clear understanding of each design: its logic, its key assumptions, and the trade-offs involved. The aim is to build the critical thinking needed to evaluate their application in published research and to adapt them to new settings.
Throughout the course, you are encouraged to connect what you learn to your own research interests. You will learn how to choose between designs based on their research question and available data, and how to tailor those designs to their own substantive needs.
We introduce the potential outcomes framework, the fundamental problem of causal inference, and the role of confounding. We discuss randomised experiments as the benchmark design, and briefly consider designs where selection into treatment is based on observables.
When confounders cannot be observed, panel data or repeated cross-sections offer a way forward. We cover the difference-in-differences estimator and its assumptions, then introduce the synthetic control method as a generalisation for settings without a suitable comparison unit.
IV methods allow causal inference in the presence of unobserved confounders. We cover the logic of IV, the LATE estimator, randomised encouragement designs, and the assumptions required for non-randomised instruments.
RDDs exploit discontinuities in treatment probability as a function of a running variable. We cover both sharp and fuzzy designs and the assumptions each requires.
You will work in small groups throughout the course to develop a design-based research proposal. On Day 5, groups present and receive feedback.
The course is structured into five live sessions, each lasting 3 hours. The first three sessions will take place on Tuesday 8 – Thursday 10 September at Jagiellonian University. The remaining two sessions will take place on Monday 14 – Tuesday 15 September, online. You must attend all sessions to complete the course.
The instructor will also conduct Q&A sessions and offer designated office hours for one-to-one consultations.
You are expected to have basic knowledge of quantitative analysis (including sampling, statistical inference, linear regression, regression models for binary outcomes, panel data), and some experience with applying those methods to large dataset.
If you need to review material on regression models, you can consult this classic textbook: Quantitative Social Science by Kosuke Imai (Princeton University Press, 2017).
This course is designed at an intermediate level.
You should expect approximately 24 hours of total engagement, including:
You will engage in a variety of activities designed to deepen your understanding of the subject matter. While daily live teaching sessions from the core of your learning experience, the learning commitment will extend beyond these. This ensures that you engage deeply with the course material, participate actively, and complete assessments to solidify your learning.
If you have registered and paid for the course, you will be given access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you can access course materials such as pre-course readings.
During the course week, participants are expected to commit time to preparing for each session, including readings and practical assignments.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.