I came across this course yesterday and thought it might be good for anyone interested in learning more about causal and statistical reasoning generally, and the use of causal graphs particularly.
The Open Learning Initiative is offered by Carnegie Mellon University and information about the course (free) is available here.
From the course introduction: “This course is meant to serve students in several situations. One, it is meant for students who will only take one such research methods course, and are interested in gaining basic skills that will help them to think critically about claims they come across in their daily lives, such as through a news article. Two, it is meant for students who will take a few statistics courses in service of a related field of study. Three, it is meant for students interested in the foundations of quantitative causal models: called Bayes Networks.”
I have added emphasis to the third intention of the course as it is the one I would hope those interested in applying causal models to a KBP for PT would fit into. ”Formally, Bayesian networks are DAGs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses.”