UV9204 – Study Design for Causal Inference

Course content

This course aims to discuss avenues, challenges, and limitations in the design of studies that intend to address causal research and evaluation questions.

The course covers experimental designs, quasi-experimental designs, and program evaluations. Key principles are illustrated with real-life example studies to improve the understanding of the assumptions and prerequisites of the methods and to critically discuss their scope.

UV9204 Study Designs for Causal Inference is the PhD-level version of MAE4054 Study Designs for Causal Inference, a course within the master's program, Assessment Measurement and Evaluation. The content, schedule and reading list for UV9204 Study Designs for Causal Inference are the same as for MAE4054 Study Designs for Causal Inference. 

Learning outcome

Knowledge:

• Recognize the core relevance and challenges in drawing causal inferences from data. 
• Understand advantages, challenges, and limitations of experimental, quasi-experimental, and evaluation study designs.
• Know central design elements and principles of experimental, quasi-experimental, and evaluation study designs.

Skills:

• Apply specific methodological procedures and techniques to analyze data in line with the study design.

Competencies:

• Critically read and evaluate the support for causal inferences in empirical studies. 
• Take a systematic and reasoned approach to designing studies, considering the advantages, challenges, and limitations of the variety of approaches in light of the central research questions and/or evaluation objectives.

Admission to the course

There is a limited number of seats due to joint teaching with the master’s level version of the course.

PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course.

The deadline for registration is on the corresponding semester page for the course. 

Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV) can apply in StudentWeb.

Other applicants can apply by filling out and sending in a electronic registration form, which is found on the corresponding semester page for the course. 

Formal prerequisite knowledge

Basic knowledge of the statistical programming environment R is required.

Recommended previous knowledge

Prior knowledge to MAE4000 Data Science or equivalent.

Overlapping courses

• 3 credits overlap with UV9296 – Methods for Causal inference in Educational Research.
• 3 credits overlap with MAE4054 – Study Designs for Causal Inference.

Teaching

This course combines lectures, seminars (including critical discussions of pro and contras of specific studies), and more practical lab activities.

Obligatory activity: 

• Presentation of a critique of an assigned study and participation in the discussion in the seminars. Once qualified for participating in the exam, you retain this qualification for the next two times the course is offered.

• Compulsory assignment.

 

Lectures are held by Professor Henrik Daae Zachrisson (ISP) and Associate Professor Astrid Marie Jorde Sandsør (ISP).

Schedule and literature: Please see the applicable semester page for the course. 

Examination

In order to qualify for the exam, you must have given a presentation of a critique of an assigned study and participated in the discussion in the seminars.

The exam is an individually written paper. In 1500-2000 words you characterize and critique an empirical study in light of the study objectives, study design, and causal inferences that were drawn with specific attention to identification issues and possible specification and robustness checks.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.