GESIS Training Courses
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Wiss. Koordination

Alisa Remizova

Administrative Koordination

Janina Götsche

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Causal Models for Qualitative and Mixed Methods Research

About
Veranstaltungsort:
Cologne / Unter Sachsenhausen 6-8
 
General Topics:
Data Analysis, Computational Social Science, Qualitative Methods
Course Level:
Advanced
Format:
Workshop
Software used:
R
Duration:
2 days
Language:
English
Fees:
Students: 220 €
Academics: 330 €
Commercial: 660 €
 
Keywords
Additional links
Dozierende: Macartan Humphreys, Alan M. Jacobs

Referenteninformationen - Macartan Humphreys

Referenteninformationen - Alan M. Jacobs

Seminarinhalt

Registration Deadline: 09.01.2026
 
This course introduces a novel framework for qualitative and mixed methods research in the social sciences, grounded in formal tools from causal inference and Bayesian reasoning. It equips you to integrate evidence across diverse data sources and to link theory and empirics in a coherent, transparent, and analytically rigorous way. We begin with a review of foundational concepts in causal inference and Bayesian updating - a versatile framework for refining theories in light of new information. Building on these tools, we show how to generate and justify inferences using within-case evidence (e.g., process tracing), cross-case correlations, or a principled combination of both. We also explore how causal models can guide research design - helping you identify which cases to study, what data to collect, and how to combine methods strategically to address complex empirical questions.
 
 
Organizational structure of the course
Each day will have two 90-minute lectures and two 90-minute exercise sessions focused on applying the learned material to your projects. Prior to the course, please do some basic preparation for the project by identifying a topic and set of relevant variables. Detailed guidance for this preparation will be distributed to enrolled students in advance of the course.


Zielgruppe

This course is relevant to graduate students, postdocs, or other researchers in social sciences who are interested in integrating quantitative and qualitative methods and connecting theory to empirical research in a systematic and transparent way using advanced causal inference methods. You should be interested in qualitative reasoning and case-level inference but also open to using quantitative methods.


Lernziel

By the end of the course, you will:
  • Have a deep understanding of different approaches to causal inference.
  • Understand how to update your beliefs about causal processes from qualitative and quantitative data.
  • Understand under what conditions credible inferences can be drawn from observational data.
  • Understand how to design better research by using causal models to choose cases, data sources, and methods that are best aligned with your questions.


    • Voraussetzungen

  • Basic knowledge of R.
  • Basic knowledge of causal inference (e.g., potential outcomes notation).
    •  
    Software and hardware requirements
    Prior to the course, you should have the most recent versions of R and RStudio installed, as well as the current version of CausalQueries, available here on CRAN.
    Installation of R: https://cran.r-project.org/
    Installation of RStudio: https://rstudio.com/products/rstudio/


    Zeitplan

    Literaturempfehlungen