About the Course
S Modern Causal Analysis in the Social Sciences (MAD, Teil I / II)
What is the effect of education on income? Has a job creation scheme created jobs? Does a low income leads to voting abstention?
These questions are causal questions: does a change in X cause a change in Y?
To identify causality, experiments with randomized control and treatment groups are regarded as the gold standard. Oftentimes in social science, only observational data which pose obstacles to causal analysis is available. As one learns in statistics, correlation in this case does not imply causation. But what does imply causation? The seminar will cover methods of modern causal analysis that are trying to overcome the problems of observational data and give an answer to that question. Specificially, the following topics are discussed:
1. the concept of causality based on counterfactuals and directed acyclic graphs (DAGs)
2. two methods for cross-sectional data: regression adjustment and propensity score matching
3. two methods for panel data: fixed effects and difference in differences
These questions are causal questions: does a change in X cause a change in Y?
To identify causality, experiments with randomized control and treatment groups are regarded as the gold standard. Oftentimes in social science, only observational data which pose obstacles to causal analysis is available. As one learns in statistics, correlation in this case does not imply causation. But what does imply causation? The seminar will cover methods of modern causal analysis that are trying to overcome the problems of observational data and give an answer to that question. Specificially, the following topics are discussed:
1. the concept of causality based on counterfactuals and directed acyclic graphs (DAGs)
2. two methods for cross-sectional data: regression adjustment and propensity score matching
3. two methods for panel data: fixed effects and difference in differences
The individual topics are presented in an accessible way not relying on mathematical knowledge. Presented methods are applied using real-world examples and applications are carried out in R.
Participants will be able to a) think causally and create DAGs, b) critically discuss methods of causal analysis c) and apply causal analysis to answer own research questions.
Voraussetzungen für Studiennachweise / Modulprüfungen
Studiennachweis: Regular, active participation and take-home excercise
Modulprüfung: Regular, active participation, take-home excercise and research paper
Lehrende
Termine
Thursday, 09.04.2020 (1. Termin) 10:00 bis 12:00 UhrGD E2/208 CIP-Pool
Anmeldung
Bitte melden Sie sich in eCampus für die Veranstaltung an.