Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.
The first part of this course is comprised of five lessons that introduce the theory of causal diagrams and describe its applications to causal inference. The fifth lesson provides a simple graphical description of the bias of conventional statistical methods for confounding adjustment in the presence of time-varying covariates. The second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Karen Carlsoncompleted this course, spending 6 hours a week on it and found the course difficulty to be medium.
I had no idea what this was when I signed up, but the teaser vid was interesting so thought I'd take a quick peek. I finished the course with a surprisingly good grade, since I have absolutely no background in data science. But the course started with absolute basics and added more elements as time went on, so I was able to continue. It was very well done, with clear explanations, great graphics (not snazzy, but clear and understandable) and lots of repetition. I would guess those who have a reason to take this course - data scientists - would find it fairly easy. And for those like me, who have no reason to take it other than curiosity, it's manageable.
FMI see my personal blog post at https://sloopie72.wordpress.com/2017/11/01/from-wtf-is-this-to-heythis-is-kind-of-fun-causal-diagram-mooc/