Diagram of causal theories.

Causal & Statistical Reasoning[Enter Course]


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.

Additional Course Details

Topics Covered:
Causation, association and independence, causation to association, association to causation: problems, association to causation: strategies
Estimated Time to Complete Course:
This is a semester long course, if you complete one or two modules per week.
Additional Software or Materials Required:
You will need to have Flash, Java, and Shockwave installed. These programs are free. More detailed information is provided in the course under “Test and Configure Your System.”
Maintenance Fee (per student):
Free for independent learners; $15 for academic students.

In-Depth Description

By adjusting the set of modules, cases, and causality lab exercises covered, instructors and students can tailor the course to their needs. The OLI project teaches annual summer workshops for faculty who are interested in learning how to integrate the material into their courses. Webinars and other resources are also available.

Open & Free Version

This Open & Free Course provides you with access to an online course comparable to a full semester course on Causal and Statistical Reasoning taught at Carnegie Mellon University. Your access includes the complete online course including all expository text, simulations, case studies, comprehension tests, computer tutors, and the Causality Lab.

At Carnegie Mellon, this online course is taught in combination with instructor-lead discussion sections. The Open & Free Causal and Statistical Reasoning course does NOT include access to the end-of-module graded exams or to the course instructor. No credit is awarded for completing the Open & Free Causal and Statistical Reasoning course.

Academic versions of this course are offered by educational institutions which award accreditation. 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. The Academic Courses track students’ learning of key concepts and give the student and the instructor informative feedback to improve learning outcomes.