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What is the difference between causal patterns and directional graphic patterns?

What is the difference between causal models and oriented graphic models?

or

What is the difference between causal relationships and directed probabilistic relationships?

or even better:

What would you introduce into the interface of the DirectedProbabilisticModel class and what is in the CausalModel class? Is it inherited from another?

Joint decision:

interface DirectedModel {
  bool NodesDependent(set<Node> nodes, map<Node, Distribution> context)
  map<Node, Distribution> InferredProbabilities(map<Node, Distribution> observed_probabilities,
                                          set<Node> nodes_of_interest)
}
interface CausalModel: DirectedModel {
  map<Node, Distribution> InferredProbabilities(map<Node, Distribution> observed_probabilities,
                                          map<Node, Distribution> externally_forced_probabilities,
                                          set<Node> nodes_of_interest)
}
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3 answers

Causation of Judea Pearl is a book to read.

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a directed graph is simply a graph (nodes and edges) that is directed (edges have directions). causal models are models that tell you how variables affect each other, one way to do this is to use directed graphs. AI research has shown that deterministic cause-effect relationships are not enough to encode the knowledge of the world around us because it is too confused. That's why probability is added to the picture.

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