Graphical Causal Models
Graphical Causal Models
An introduction to essential terminology and ways of using causal graphs to represent causal systems. 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.
What students will learn:
- Represent direct causes and effects via causal graphs.
- Represent direct and indirect causation with causal graphs.
- Represent common causes and effects with causal graphs.
- Represent feedback with causal graph.
- Identify the effects of hard and soft interventions on causal graphs.
- Represent and compute undirected paths.
- Recognize colliders and the number of them in a path.
- Recognize Treks.
- Categorize nodes on a path as active / inactive.
- Categorize paths as active / inactive.
- Categorize d-separation / d-connection for any X, Y | { Z } in a causal graph.
- Connect Conditional Probability Table (CPT) Structure to a Graph.
- Encode parametric form into Conditional Probability Table (CPT).
Duration: Not defined
Level: Not defined
Certification: No
Cost: Free
Language: English
Type: Self-Paced
Please note: these courses are provided by external sources, links are not actively managed or regularly updated, content might be moved or unavailable.
