14335.0208 23S 2SH PT Causal Inference and Public Policy in Europe   Hilfe Logo

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Causal Inference and Public Policy in Europe 
Summer semester 2023
Political Science
(Contact information)
Anzahl der Zuordnungen zu laufenden und auslaufenden Studien ausblenden 
Zuordnung zu Modul 
Art Empf.
Prüfungsart Äquiv. Vorauss.
laufend 2022/23
Doctoral studies
64 D57 Research in Management, Economics and Social Sciences - Social Sciences (HG-NRW)
64 D57 Research in Management, Economics and Social Sciences - Social Sciences (HG-NRW)
64 D57 Research in Management, Economics and Social Sciences - Social Sciences (HG-NRW)
Master's programme
88 275 Political Science (HG-NRW)
auslaufend 2022/23
Master's programme
88 275 Political Science (HG-NRW)
Allocations: 2 
Angaben zur Abhaltung

Social Sciences have undergone a “causal revolution” in the last few decades. The need to find reliable answers for important questions such as “Do higher minimum wages increase unemployment?” or “Do violent protests reduce support for a social movement?” has led researchers to develop a rigorous toolkit of methods and techniques to understand the causal relation between X and Y. This course starts with the main contemporary theoretical framework behind current causal analysis in social sciences, the potential outcomes framework. We talk about how causal relations are understood in terms of counterfactuals, and the assumptions necessary to identify causal effects, as well as common challenges and pitfalls. The course focuses on statistical inference for the analysis of public policy across a wide range of contexts,and covers issues related to the design, implementation, and evaluation of policy changes. Technical aspects will focus on computational approaches and real-world challenges.
Students are expected to be familiar with basic statistical methods for analysis and
inference (i.e., run and interpret a linear regression). You should have taken Introduction to Quantitative Methods or a similar course before starting this. Students
should also have a basic familiarity with R.
Students will learn to model cause-and-effect relationships and develop counterfactual scenarios. They will gain experience using computational methods to evaluate and predict the impacts of policies, interventions, and events, while learning to avoid common pitfalls. By the end, students will be able to: (1) think through which of the methods covered in class (if any) would be best suited to solve a given decision problem and what data would be required; (2) perform appropriate analysis and interpret results; (3) connect those results to strategic decision-making; (4) critically examine statistical causal claims put forward by others; and (5) present findings and recommendations effectively for audiences of varying sophistication.
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Angaben zur Prüfung
siehe Stellung im Studienplan
Note: Anmerkung: Für Informationen zu Prüfungsmeldung (Vorgehen, Fristen, etc.) beachten Sie bitte unbedingt die Hinweise des Instituts: http://www.politik.uni-koeln.de/exam.html

For important information on the organization and management of examinations (registration, deadlines) please cf. to our Department: http://www.politik.uni-koeln.de/exam.html
online information
course documents