STAT3230 Causal Inference (5 op)
Verkosto-opintojakso
Verkosto: Vaasan korkeakoulukonsortio
Verkosto: Matematiikan ja tilastotieteen syventävien kurssien ristiinopiskelu
Tämä opintojakso on tarjolla Matematiikan syventävät opinnot -ristiinopiskeluverkostossa. Verkoston opinnot ovat tarjolla seuraaville opiskelijoille:
- Matematiikan kandidaattiohjelma
- Matematiikan maisteriohjelma
- Matematiikan aineenopettajien kandidaattiohjelma
- Matematiikan aineenopettajien maisteriohjelma
- Matematiikan, kemian tai fysiikan aineenopettajan ja luokanopettajan kandidaattiohjelma (matematiikan opintosuunta)
- Matematiikan, kemian tai fysiikan aineenopettajan ja luokanopettajan maisteriiohjelma (matematiikan opintosuunta)
- Matematiikan ja tilastotieteen tohtoriohjelma
- Matemaattisten tieteiden ja luonnontieteiden tohtoriohjelma (matematiikan opintosuunta)
Kuvaus
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.
Osaamistavoitteet
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
Esitietojen kuvaus
Understanding of regression analysis and applied inferential statistics.