Modern Causal Analysis in the Social Sciences (MAD)

Kurs-Nr.: 080387 | Zeit: Do 10-12 | Raum: GD E2/208 | Semester: SoSe 2020


Aktuelle Änderungen

Die Veranstaltung wird als Webkonferenz durchgeführt. Weitere Informationen zum Ablauf der Veranstaltung erhalten Sie im Moodle-Kurs.

Das Passwort für den Moodle Kurs erhalten alle über eCampus angemeldeten Studierenden vorab per E-Mail.

Registration via eCampus starting 01.03.2020
Participants should have a basic understanding of statistics and multiple linear regression analysis. Prior knowledge of R and/or causal analysis is not neccessary.

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

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.

Angrist, J D, and Pischke, J S. (2008). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, New Jersey: Princeton University Press.

Best, H., & Wolf, C. (2014). The SAGE handbook of regression analysis and causal inference. (Chapter 12 & 15). London: SAGE Publications Ltd. doi: 10.4135/9781446288146

Imai, K. (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.

Keele, L. (2015). The Statistics of Causal Inference: A View from Political Methodology. Political Analysis, 23(3), 313-335.

Morgan, S., & Winship, C. (2014). Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge: Cambridge University Press.

Pearl, J. and Mackenzie, D. (2019). The book of why: the new science of cause and effect. New York: Basic books.

Voraussetzungen für Studiennachweise / Modulprüfungen:
Studiennachweis: Regular, active participation and take-home excercise
Modulprüfung: Regular, active participation, take-home excercise and research paper