Propositional Causal Bayesian Network Generator
Overview
The Propositional Causal Bayesian Network Generator builds a quantified causal model, a Bayesian network or Risk-Centric Causal Network, directly from the content of a Knowledge File. It proposes nodes, causal arcs with mechanism comments, causal effects, conditional probability tables, and prior probabilities for root causes.
Use it for document-derived Causal Bayesian Networks and risk-centric models.
Inputs
- Knowledge File content
- Optional General Context
Outputs
- Nodes
- Causal arcs
- Mechanism comments
- Causal effects
- Conditional probability tables
- Root priors
Workflow
Prepare the network, source material, selected nodes, selected arcs, or Knowledge Files required by the function.
Confirm that Hellixia provider settings and model access are configured.
Open
Hellixia > Document Analysis > Propositional Causal Bayesian Network Generator.Review the available options and add General Context when the model needs domain-specific guidance.
Run the function and inspect the generated graph, comments, priors, embeddings, translations, or images.