Course detail

Welcome to Explanatory modeling of observational quantitative data: Causal graphs, interactions, and research design choices

Description:

"Explanatory modeling of observational quantitative data " is an applied course designed for PhD students

in quantitative social sciences who wish to deepen their understanding and skills in working with observational quantitative data (e.g., non-experimental data). Causality is hard to establish with observational data, yet a theory-testing approach still forces researchers to formulate causal theoretical models to motivate their statistical choices. This course works with this tension in quantitative social science research and engages with applied recommendations for best practice.

The first day of the course introduces directed acyclic graphs (DAGs), discusses the need for causal reasoning for regression-based modeling, and offers training for developing DAGs.

The second day is dedicated to further complexities emerging from interactive and non-linear hypotheses, as well as mediation relationships, introducing statistical packages to ensure valid inferences for these statistical scenarios.

The third day introduces approaches for ‘causal’ research designs to observational data, focusing on matching estimators and different regression-based approaches (e.g., instrumental variables). We compare these to regression-based approaches and discuss strengths and weaknesses. We end the third day with a Lab session that provides the chance to apply some of the content to your own research.

The course expects a basic familiarity with quantitative methods (e.g., linear regression). The applied statistical teaching is done with R, and students are recommended to have basic knowledge of R programming. Most of the course content, however, can also be followed with STATA (e.g., similar/same packages in STATA). Students are encouraged to bring their own research questions to the course and engage in potential inferential/modeling challenges in their field during the practical parts of the course.

Through a combination of lectures, practical exercises, and case studies, students will engage with current best practices in observational quantitative social science research. You will learn about the use of directed acyclic graphs. You will explore various statistical tools, such as kernel or bin plots of linear interactive relationships, as well as approaches for causal mediation analysis. Finally, you will be enabled to make informed choices of whether matching or regression-based approaches might be useful tools for your analysis.

By the end of the course, you will have a comprehensive toolkit of advanced statistical approaches to tackle complex research questions in quantitative social sciences. You will also gain the ability to critically evaluate existing literature and design rigorous empirical studies using observational data.

Teaching methods:

• Lectures, practical exercises, and case studies
• Applied programming with R (Lab Sessions)
• Illustration of the statistical approaches with real world cases and data
• Opportunities to work with your own data during the course

Program outline:

1st day (with Dominik Schraff)

08:00-10:00 Inference to the best explanation (lecture)

10:00-12:00 Directed acyclic graphs for regression-based analysis (lecture)

13:00-16:00 Developing and presenting a DAG

2nd day (with Dominik Schraff)

08:00-10:00 ‘Causal’ mediation analysis (lecture)

10:00-12:00 Lab Session mediation analysis

13:00-15:00 Interaction effects (lecture)

15:00- 16:00 Lab Session interaction effects

3rd day (with Jochen Reiner)

08:00-10:00 ‘Causal’ designs to observational data: Matching (lecture)

10:00-12:00 Regression-based approaches

13:00-15:00 Lab Session matching and regression-based approaches

15:00- 16:00 Recap and Questions

Organizer: Dominik Schraff

Lecturers:

• Dominik Schraff, Department of Politics and Society, Comparative Politics Group
• Jochen Reiner, Business School, Marketing and Market Processes Group

Description of paper requirements:

The final paper is a post-reflection paper that should be sent to the teacher after the course. The paper should present the whole workflow of (1) formulating a hypothesis/argument, (2) producing a directed acyclic graph, (3) acquiring and preparing the data to test the argument, (4) specifying and justifying the statistical model to test the hypothesis, and (5) assess the statistical validity of the findings.

The emphasis should lie on points 2, 4, and 5. Therefore, students are encouraged to use readily available data, e.g. replicating existing studies that relate to the methods covered in this course or working with existing data from their research. Students will receive feedback from the teacher on their post-reflection paper

ECTS: 3

Date: 27, 28, 29 May 2026

Place: Aalborg University

City: Aalborg

Number of seats: 15

Deadline: 7 May 2026

Important information concerning PhD courses:

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course. We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.