An introduction to essential terminology and ways of using causal graphs to represent causal systems. Learn about Open & Free OLI courses by visiting the “Open & Free features” tab below.
Graphical Causal Models — Open & Free
- Description
- Open & Free features
- What students will learn
- Learning objectives by module
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Description
In making the causal graph modules, we’ve taken a very spare approach and cover only the essential ideas in terminology on causal graphs. They include the basic concepts of causal graphs as a way to represent causal systems, but they don’t go into nuance or extended case studies.
In the modules, we present graph theoretic ideas of directed paths, undirected paths, and treks. We go all the way through D- Separation, which is a fundamental notion developed by Judea Pearl and colleagues in the late 1980s. We present the key ideas in just a 2- to 4-minute video followed immediately by several Learn By Doing exercises to see if you’ve got the ideas presented in the video. The activities contain feedback and may include several layers of hints to help you if you get confused. The entire unit through Bayes Nets should take no more than three hours.
We hope you enjoy the material, and we are confident that learning this content will help with any more extensive investigations into graphical causal models.
