Causal inference

The course is a compulsory course within the Master’s Programme of Applied Biostatistics (M2STA).

The entry requirements of the course include a professional degree/Bachelor's degree of at least 180 credits in health sciences, natural sciences, economics, or engineering, and a course in statistics or quantitative methods of at least 7,5 credits. Further, English B/English 6 or equivalent, and Matematik 3b/3c or equivalent are required.

This course introduces key concepts and methods in causal inference, with a focus on applications in health sciences. Both conceptual frameworks and statistical methods are discussed. The course covers how to formulate causal questions in the potential outcome framework, draw causal diagrams, identify sources of confounding and bias, and apply appropriate strategies for estimating causal effects using observational and experimental data. Topics covered include mediation, propensity score methods, and more advanced techniques such as simulation-based approaches like the g-formula. Throughout the course, emphasis is placed on critically evaluating the assumptions required for causal interpretation of the results and understanding the consequences of these assumptions for applications, such as policies and interventions. The course also introduces tools for evaluating the robustness of causal results. The course provides an opportunity to reflect on the use of causal inference as a basis for evidence-based decisions related to sustainable development in areas such as good health and equality in health matters. Examples and data that highlight challenges in such analysis, such as the role of sociodemographic factors in causal chains, will be used.

The course is sustainability focused.

On successful completion of the course the student will be able to:

Knowledge and understanding

• Describe key concepts and theoretical frameworks used in causal inference.
• Explain key assumptions required for causal interpretation of relationships in observational and experimental studies.

Competence and skills

• Construct and interpret graphical representations of assumed causal relationships.
• Identify and apply appropriate methods for estimating causal effects under different study designs.
• Use statistical software to implement techniques for causal inference and interpret the results in context.

Judgement and approach

• Critically evaluate the validity of causal claims in observational and experimental research.
• Reflect on the strengths and limitations of different methods of causal inference in relation to data quality, assumptions, and relevance to the issue at hand.
• Reflect on the role that causal inference plays in supporting evidence-based decisions that contribute to sustainable development and equitable health outcomes.

The course combines lectures, seminars, and computer exercises.

Language of instruction: English

The course is examined through an individual written exam (5.5 credits) and four compulsory computer-based exercises (4 x 0.5 credits). Completion of non-approved compulsory elements will be offered and is to be carried out according to the teacher's instructions.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

The grading scale comprises: Pass with distinction (VG), Pass (G) and Fail (U). The compulsory computer-based exercises are graded Pass (G) and Fail (U), while the individual written exam is graded Pass with Distinction (VG), Pass (G), and Fail (U).

To receive a Pass grade for the course, students must receive a Pass grade for both the compulsory exercises and the written exam. To receive a Pass with Distinction grade for the course, students must receive a Pass grade for the compulsory components and a Pass with Distinction grade for the individual written exam

The course evaluation is carried out in the form of an anonymous questionnaire. A compilation of the questionnaire is done by the course coordinator. The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.

Each student needs access to a portable computer with R and RStudio (or other user interface for R) installed (a minimum of 8GB RAM is recommended).