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PhD Toolbox Course : Estimating Causal Effects with Observational Data
Provider: Faculty of Science

Activity no.: 5226-25-03-01There are 25 available seats 
Enrollment deadline: 28/04/2025
PlaceVon Langen F111
Rolighedsvej 23, 1958 Frederiksberg C
Date and time19.05.2025, at: 09:00 - 23.05.2025, at: 16:00
Regular seats30
LecturersArne Henningsen
Bo Markussen
ECTS credits5.00
Contact personCharlotte Bukdahl Jacobsen    E-mail address: cja@ifro.ku.dk
Enrolment Handling/Course OrganiserArne Henningsen    E-mail address: arne@ifro.ku.dk
Semester/BlockSpring
Course workload
Course workload categoryHours
Lectures25.00
Practical exercises10.00
Preparation34.00
Report writing69.00
Sum138.00
Aim and content
Researchers are usually interested in investigating causal relationships, i.e., how one thing affects another thing. While the analysis of causal relationships is easiest when using experimental data, in several research areas (e.g., social sciences) experiments are not always feasible, and when they are feasible, they may suffer from important limitations. As a result, most empirical studies in the social sciences and in related research areas are based on observational (i.e., nonexperimental) data.

Participants in this course will learn state-of-the-art empirical methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs and empirical methods (“identification strategies”) for analysing causal relationships, and they will learn to choose the most appropriate research designs and empirical methods for analysing a specific research question. All this will help participants obtain more credible and reliable results in their empirical work and to publish their work in better journals.

The methods that will be taught in this course include, e.g., directed acyclic graphs, methods based on instrumental variables, synthetic control methods, regression discontinuity design, difference-in-differences, methods for panel data with staggered treatment, causal machine learning methods, etc. The course participants will learn the theoretical background and underlying assumptions of these methods as well as to apply them in
real-world empirical analyses.

Learning outcome
Knowledge:
• Describe various methods for analysing causal research questions with observational data.
• For various methods for analysing causal research questions with observational data, describe the assumptions that need to be fulfilled if the respective method should give reliable estimates of the causal effect.

Skills:
• Apply various methods for analysing causal research questions with observational data using (statistical) software such as R, Stata, or Python.
• Assess to which extent assumptions that are required by various methods for analysing causal research questions with observational data are fulfilled in specific real-world applications.

Competences:
• Choose research designs and methods (“identification strategies”) that are appropriate for analysing various causal research questions with observational data in their research area.
• Critically evaluate the appropriateness of research designs and methods (“identification strategies”) for analysing various causal research questions with observational data in their research area (this refers to their own research, e.g., when discussing strength and weaknesses of their empirical analyses in their own papers, as well as to the research done by others, e.g., when reviewing manuscripts or assessing the reliability of research done by others for other reasons).

Literature
The participants will be informed about the course literature at least four weeks before the course starts. The course literature could be, e.g.,
• Angrist, J.D. and Pischke, J.-S. (2009), Mostly Harmless Econometrics, Princeton University Press.
• Angrist, J. D. and Pischke, J. S. (2014). Mastering ‘Metrics: The path from Cause to Effect. Princeton University Press.
• Henningsen, A., Low, G., Wuepper, D., Dalhaus, T., Storm, H., Belay, D. and Hirsch, S (2024): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. IFRO Working Paper 2024/03, Department of Food and Resource Economics, University of Copenhagen. https://EconPapers.repec.org/RePEc:foi:wpaper:2024_03
• Morgan, S.L. and Winship, C. (2014), Counterfactuals and Causal Inference: Methods and Principles for Social Research, 2nd ed. Cambridge University Press.
• Journal articles.

Teaching and learning methods
The course participants should read the course material before the course starts to be well prepared for the course. The course consists of lectures, in which various methods for analysing causal research questions with observational data as well as their underlying assumptions are presented and explained. The participants of the course will also do practical exercises, in which they learn to implement these methods in practice. While the teachers will use the R software to present solutions to these exercises, the participants are free to use other software packages (e.g., Stata, Python, ...). The practical exercises also include group discussions, e.g., about the appropriateness of research designs and empirical methods (“identification strategies”). The course participants can choose to write a short report, in which they apply at least one of the methods taught in the course to real- world observational data, e.g., a part of the analyses that they do in their PhD project. Reproducibility of the empirical analysis will play a key role in
the lectures, the practical exercises, and in the ‘short report’ (exam).

The participants get this course approved with 2.5 ECTS if they attend the lectures, do the practical exercises, and pass a multiple-choice test given at the end of course.
The participants get this course approved with 5 ECTS if they additionally write and submit a short report (see above) that is positively assessed by the teachers (e.g., so that decent journals in the respective research area
would assess the quality of the empirical analysis to be appropriate).

Remarks
For all participants there is a participant fee of 1000 DKK that covers coffee, tea, and lunch all days

Cours fee:

- No corse fee for PhD students enrolled at SCIENCE
- No course fee for PhD students enrolled at Danish PhD schools that are members of the open market for PhD courses
-1200 DKK - PRICING PER ECTS PER PARTICIPANT: PhD students enrolled at Danish PhD schools that are not members of the open market for PhD courses (CBS and Graduate School of Business and Social Sciences AU)
- 1200 DKK - PRICING PER ECTS PER PARTICIPANT PhD students enrolled at foreign universities

Some participants have to additional pay a course fee, see: https://science.ku.dk/phd/courses/databases/Pricing_PhD_courses_at_SCIENCE_2024.pdf

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