Handbook

This course in statistics that covers the design and analysis of experiments and observational studies to answer causal questions. Topics include potential outcomes, causal estimands, and the importance of the design phase in both experiments and observational studies.  Experimental design topics will include randomisation techniques, such as blocked and factorial experiments, and analysis methods, including both parametric and randomisation methods. Causal inference topics will include propensity scores, weighting, and matching.  Students will put methods into practice using the statistical software R throughout the course.

The intended audience of this course is wide-ranging, from statistics and data science students to students in the social and natural sciences (biology, psychology, economics, and others).  Prior knowledge of basic probability and introductory statistics, including linear regression, is necessary.

This course is jointly taught at two levels.  MATH3852 is for 3rd year undergraduates, whereas MATH5852 is for Honours & Masters students.