Causal and Statistical Reasoning

This course provides an introduction to causal and statistical reasoning. After taking this course, students will be better prepared to make rational decisions about their own lives and about matters of social policy. They will be able to assess critically—even if informally—claims that they encounter during discussions or when considering a news article or report. A variety of materials are presented, including Case Studies where students are given the opportunity to examine a causal claim, and the Causality Lab, a virtual environment to simulate the science of causal discovery. Students have frequent opportunities to check their understanding and practice their skills.

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.

In-Depth Description

Topics Covered:

Causation, association and independence, causation to association, association to causation: problems, association to causation: strategies

Academic Version

Academic versions of this course are offered by educational institutions that award accreditation, and use the Causal and Statistical Reasoning OLI course as a textbook replacement or supplement. Students taking an Academic Course have access to the same course materials as the students taking the Open & Free Course PLUS access to graded exams, and instructors have access to a tools and features that are unavailable when using the Open & Free version. The Academic Courses track students’ learning of key concepts and give the student and the instructor informative feedback to improve learning outcomes.