Graduate Prerequisites: Students must be in a doctoral program and have completed EP854 (or equivalent from another institution) and have SAS or R programming skills equivalent to BS805 or above. This course introduces advanced causal modelling methods in data analysis, with the aim of providing students with the ability to recognize situations in which the use of such methods may be beneficial, knowledge of how to implement the methods, and an understanding of the strengths and limitations of each method. The course covers approximately 5 to 6 analytic methods in a series of 2- or 3-session modules. Topics may vary slightly in different semesters; examples include propensity scores, marginal structural models, mediation analysis, simulation methods, quantitative bias analysis, instrumental variables, regression discontinuity, machine learning and Bayesian analysis. Hands-on sessions in the classroom, homework assignments, and a final data analysis project provide students with practice in the conduct of analyses using these methods.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.