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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.
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