Across the social sciences, questions of causal inference are attracting increased attention, with researchers wanting to not only test associative hypotheses but uncover causal relationships between two or more variables. With social-science contexts often not lending themselves to classical experimental designs, recent years saw the development of advanced methodological approaches to investigate such causal relationships from observational data, and the advent of big data has brought forward further methodological advances in relation to machine learning. In 2021, the GESIS Spring Seminar will address these developments and offer three courses on causal inference. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data.
Week 1 (March 1 - 5): Causal Inference and Experiments
Asst. Prof. D.J. Flynn, IE University Madrid (Spain)
Week 2 (March 8 - 12): Causal Inference in Observational Studies
Dr. Krisztián Pósch, University College London (United Kingdom)
Thiago R. Oliveira, London School of Economics (United Kingdom)
Week 3 (March 15 - 19): Causal Machine Learning
Asst. Prof. Dr. Michael C. Knaus, University of St. Gallen (Switzerland)
Gabriel Okasa, University of St. Gallen (Switzerland)
Courses will be held online and can be booked either separately or as a block. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend that you register early. Thanks to our cooperation with the Cologne Graduate School in Management, Economics and Social Sciences (CGS) of the University of Cologne, doctoral students can obtain 3 ECTS credit points per one-week course.
For registration, please visit our website and sign up here!