Microcredential komex: Causal Inference and Observational Research – Designing Robust Counterfactuals


Take this course to learn how to design a robust social scientific research project that links sound social scientific theorizing to causal inference based on observational data — whether qualitative or quantitative.

What Is This Course About?
This course provides you with the skills to design and conduct a robust social scientific research project that links sound social scientific theorizing to causal inference based on observational data—whether qualitative or quantitative. To this end, we will be working closely with participants’ own research projects and applied examples. Making the most of the experimental turn, we learn how to adapt its counterfactual view of causation to working with observational data under the imperfect conditions characteristic of social, as opposed to natural, phenomena.

Learning Goals
At the end of the week, you will:

  • Be aware of the methodological discussion on causation in philosophy of the social sciences—especially the counterfactual understanding — and the potentials and limits of causal explanation and inference concerning social phenomena.
  • Know how to formulate precise and testable causal theories that cover effects, mechanisms and that have clear scope conditions.
  • Have an overview on how to draw valid causal inferences based on a counterfactual understanding of causation across the repertoire of qualitative and quantitative observational methods of causal inference.
  • Make an informed choice among, and be able to defend your research design against, possible alternative designs.
  • Know how to transparently integrate theory, counterfactual truth conditions, design choices and empirical evidence in your writing.

Assignments for the Course
4 ECTS are granted upon completion of preparations before the course (readings and pre-course exercise), active participation during the course week, and two kinds of written assignments: a) 4 out of 5 daily assignments and b) a take-home paper (research design of own project) of 4000-5000 words.


  •         09:00-10:30h: Course
  •         10:30-11:00h: Break
  •         11:00-12:30h: Course
  •         12:30-13:30h: Lunch break
  •         13:30-14:30h: Course

Recommended Readings for the Course

  • Keele, Luke (2015). The statistics of causal inference: A view from political methodology. Political Analysis, 23(3), 313-335.
  • Little, Daniel. (2023). Causation in the Social Realm, In: Alessia Damonte & Fedra Negri (Eds.), Causality in Policy Studies. A Pluralist Toolbox, Springer Open Access: https://doi.org/10.1007/978-3-031-12982-7.
  • Rohlfing, Ingo & Zuber, Christina Isabel (2021). Check your truth conditions! Clarifying the relationship between theories of causation and social science methods of causal inference. Sociological Methods and Research 50(4): 1623–1659.

Who Is Your Instructor?
Christina Zuber is full professor of German Politics at the Department of Politics and Public Administration at the University of Konstanz. She studied Political Science, Slavic Languages and Philosophy at the University of Cologne, where she also received her doctorate. As a convinced methodological pluralist, she has long- standing experience in working with both quantitative and qualitative methods of social research, and her expertise in philosophy has allowed her to contribute solutions for improving the quality of causal inference across methods.

Bildungszeit (can be claimed by employees in Baden-Württemberg) 
Anforderungen des Bildungszeitgesetzes Baden-Württemberg sind erfüllt
460 EUR / Early bird 390 EUR / Please note: you will gain access to our learning management system Moodle only after having paid your course fee
ECTS Credits 
Contact for Questions 
26.02.2024 (All day)
27.02.2024 (All day)
28.02.2024 (All day)
29.02.2024 (All day)
01.03.2024 (All day)
5 study days
No prior knowledge is required other than basic knowledge of empirical research design, such as what is a case, what is a variable. While not a requirement, students will benefit most from this course if they bring their own research in progress to work on.