STAT3230 Causal Inference (5 cr)
Cooperation network course
Network: Vaasa Higher Education Consortium
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Description
The course introduces the principles and practice of causal inference, focusing on how to understand cause-and-effect relationships through rigorous methods and study design. It begins with the philosophical and statistical foundations of causality, then emphasizes the importance of careful causal thinking and well-justified assumptions. Central to the course is the development of objective study designs, which underpin trustworthy scientific research, along with strategies for addressing challenges in real-world data. It highlights causal inference as a missing data problem governed by an “assignment mechanism,” and examines when regression methods can and cannot be reliably used for estimating causal effects. Finally, the course addresses the difficulties of drawing valid conclusions, especially with human subjects, through techniques such as sensitivity analysis and by considering both observational and experimental data contexts.
Learning outcomes
By the end of this course students learn about the first principles of causality, become familiar with theoretical foundations of causal inference and methodological challenges when analysing cause-and-effect relationships. Students learn about:
• what is ‘causal’ in statistical terms,
• how to develop an objective causal design,
• which are the methods and techniques required for developing a causal design,
• what are different types of biases and what are the ways to mitigate them,
• what is the role of causal assumptions and how to assess their plausibility,
• how to proceed with analysis of data that originates from causal designs; and,
• the importance of thoughtful conclusion-making when interpreting causal inference results
Description of prerequisites
Understanding of regression analysis and applied inferential statistics.