komex: Causal Inference and Observational Research – Designing Robust Counterfactuals

Content 

COVID-19 pandemic: Please note!
While we intend to hold the in-person courses on site, we stand ready to switch to on-line provision of these courses if the corona rules do not allow for presence teaching.

Course Structure
09.00-10.30h: Lecture
10.45-12.15h: Lab / group work
13.30-14.30h: Office hour / independent work (reading, exercises)

Content
This course provides you with the skills to design and conduct a robust social scientific research project that links sound theorizing to causal inference based on observational data—whether qualitative or quantitative. Making the most of the experimental turn, we learn how to adapt its counterfactual view of causation to working with observational data under imperfect, real-world conditions. As a special feature, you also learn how to effectively communicate your design and causal insights in your writing (be it for a PhD, an article or a book manuscript).

Topical sessions include:

  1. Philosophy of causation & specific challenges of causal inference in the social sciences
  2. Models, graphs, effects and mechanisms: causal theorizing in the social sciences
  3. The repertoire of quantitative designs
  4. The repertoire of qualitative designs
  5. Integrating theory, design and inferences in your writing

Intended Learning Outcomes
At the end of the week, you will:

  • Be aware of the methodological discussion on causation in philosophy of the social sciences and of the potentials and limits of causal explanation and inference about 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.


Examination

  • Careful preparation of readings and active participation throughout
  • completion of 4 daily tasks
  • a take-home paper that applies the content of the course to participants’ own research projects


Expectation Management
Note that this is a methodological course about designing and presenting research projects, not a methods course about how to execute data analysis under a specific method of causal inference. This course serves to guide you in making informed choices from the repertoire of quantitative and qualitative research designs and in justifying your choice against alternatives. For this aim, we will cover a broad repertoire of methods used to draw causal inferences from observational data but only with regard to how each of these methods ensures that we can infer whether x causes y.

Core Readings
Jaccard, James & Jacoby, Jacob (2010). Theory construction and model-building skills. A practical guide for social scientists. New York.
Morgan, Stephen L., and Christopher Winship (2015). Counterfactuals and causal inference. Cambridge University Press.
Rohlfing, Ingo & Zuber, Christina Isabel (2019). Check your truth conditions! Clarifying the relationship between theories of causation and social science methods of causal inference. Sociological Methods and Research. DOI: 10.1177/0049124119826156.

Course Management 
Fee 
430 EUR / Early bird 390 EUR
ECTS Credits 
4
Contact for Questions 
Seminar Room 
D 431
Date 
14.03.2022 09:00 to 14:30
15.03.2022 09:00 to 14:30
16.03.2022 09:00 to 14:30
17.03.2022 09:00 to 14:30
18.03.2022 09:00 to 14:30
Requirements 
Participants should be working on research projects that aim at causal inference/explanation based on observational data (qualitative and/or quantitative). They should be in the earlier phase/max. first half of the project, i.e. when research design choices are yet to be made, or still open to some adaptation. Basic knowledge of empirical social research is assumed, such as what is theory, what is data, what is quantitative, what is qualitative. Participants should bring their own laptops — sockets will be provided.