50th GESIS Spring Seminar: Causal Inference

The 50th GESIS Spring Seminar 2021, held virtually via Zoom from 1 to 19 March 2021.

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)


Information and registration on Gesis website !




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