My teaching explores the conditions required for causal inference and the methods (study design, data analysis) to make causal inferences when those conditions are met. Because I am an epidemiologist, the subject-matter of my courses is that of medicine and public health, and my target audience is researchers and clinicians who need to evaluate or generate evidence to support decision-making. My aim is to help them develop critical thinking, problem-solving skills, and the ability to articulate sound arguments.
At Harvard I lead core methods courses in several graduate programs. I teach clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health. I used to teach clinical data science at the Harvard Medical School and now teach in several programs of the Harvard Medical School Office of Global Education. My colleagues and I also offer a short course in June in Boston.
Through HarvardX and edX, I offer the online course Causal Diagrams: Draw Your assumptions Before Your Conclusions. This course, a MOOC, is free and accessible to anyone with an internet connection. The Assessments for the course are available here and the answers (don’t cheat, please) here. If you are interested in how a MOOC is made, Harvard Magazine published a short piece about our course.
Outside Harvard, I often teach short courses. This is my 2020 schedule:
- January: Swiss Epidemiology Winter School, Wengen, Switzerland
- March: Karolinska Institutet, Stockholm, Sweden (postponed to Fall 2020)
- May: University of Utah, Salt lake City, UT (postponed to 2021)
- June: Graduate Summer Institute of Epidemiology and Biostatistics, Johns Hopkins University, Baltimore, MD (online only)
- August: Erasmus Summer Programme, Rotterdam, the Netherlands (online only)
- September-December: City University of New York (CUNY) School of Public Health