Universität Wien FIND
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400010 SE Causal Inference for Observational Data (2021W)

SE Methods for Doctoral Candidates

Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 15 participants
Language: English


Classes (iCal) - next class is marked with N


Friday 15.10. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Friday 29.10. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Friday 12.11. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Friday 26.11. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Friday 10.12. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Friday 07.01. 09:45 - 13:00 PC-Seminarraum 3, Kolingasse 14-16, OG02


Aims, contents and method of the course

This course provides an overview of different advanced quantitative methods that are used in the social sciences to draw inferences about causal relationships from large-N observational data. We first introduce Neyman and Rubin’s “potential outcomes framework” of causality that forms a theoretical basis for the course and then survey different classes of popular methods for causal inference. These may include:

1) Matching methods
2) Instrumental variables
3) Difference-in-differences
4) Synthetic control
5) Regression discontinuity designs

These methods claim to advance on standard regression models by adjusting for selection bias on observables and unobservables. With regard to each method covered, we will address its theoretical foundations and assumptions, practical considerations and challenges, critical discussions of applications, implementation in software as well as interpretation of results.

Most sessions will consist of 1) an interactive lecture element, in which you participate through live polls and mini tasks; 2) a computer lab, in which you practice the implementation of the methods in R (or STATA); 3) a discussion of an application of the method in a published research paper.

While this is a course in advanced quantitative methods, no prior knowledge of causal inference methods is expected. A basic understanding of quantitative methods (e.g. multiple regression analysis) is desired, but students with strong motivation may also acquire this knowledge in parallel to the course. The first session of the course will provide a quick review of multiple regression analysis. A solid understanding of research design in the social sciences is assumed. Some prior familiarity with R is an asset; some familiarity with STATA is helpful.

Upon successful completion of the course, you will be able to:
• Critically think about questions of causal inference according to the potential outcomes framework
• Develop and assess causal identification strategies for your own research questions
• Implement basic causal inference analyses in statistical software
• Assess causal identification strategies in published papers in the social sciences
• Expand your knowledge on more advanced causal inference methods or extensions of the presented methods in self-study

Assessment and permitted materials

• Active participation and contribution in class (15%)
• Five critiques (approx. 150 words each) of published articles (15%)
• Take-home exam, including questions about different methods and small analysis tasks (25%)
• EITHER a Research design for a planned paper OR an Analysis report for a planned paper (45%, about 3,500 words)

Students should attend at least 80% of the sessions.

Minimum requirements and assessment criteria

Students have to pass each assessment part (see above) to obtain a positive grade for the course.

Examination topics

Topics will include materials covered in class and/or on the reading list. Some assessments may also demand students to research something themselves or collect material themselves. Research designs and Analysis reports will involve topics chosen by the students, depending on their own research interest.

Reading list

The following textbooks cover several topics of the course and can be used as reference throughout:

• Angrist, Joshua D., and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press.
• Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven: Yale University Press.

Specific readings for each class will be announced at the beginning of term.

Association in the course directory

Last modified: We 24.11.2021 10:10