Automatic Causal Network Generator

Menu path: Hellixia > Automatic Causal Network Generator

What it does

Creates an initial causal Bayesian network from a prompt-defined problem domain.

Treat Hellixia output as a structured starting point for analyst review, expert refinement, learning, inference, or communication. Generated causal directions and quantification should be checked before they are used operationally.

When to use it

• Rapid causal model prototyping
• Risk-centric causal modeling
• Starting from a domain question

Inputs

• Prompt or question
• Optional General Context
• Optional modeling constraints

Outputs

• Nodes
• Causal arcs
• Arc comments
• Causal effects
• Conditional probability tables

Step-by-Step Workflow

1. Prepare the network, source material, selected nodes, selected arcs, or Knowledge Files required by the function.
2. Confirm that Hellixia provider settings and model access are configured.
3. Open Hellixia > Automatic Causal Network Generator.
4. Review the available options and add General Context when the model needs domain-specific guidance.
5. Run the function and inspect the generated graph, comments, priors, embeddings, translations, or images.
6. Edit the output in BayesiaLab before using it for analysis, publication, or decision support.

Review Guidance

Check whether the generated labels, relationships, comments, classes, probabilities, or causal effects match the source material and expert understanding. For causal outputs, verify that the proposed direction and mechanism are plausible and that no important confounder or alternative explanation has been hidden by the generated structure.

Related Functions

• Automatic Semantic Network Generator
• Causal Network Generator
• Causal Structural Priors

Examples

• Causality — Causal Semantic Diagram, Causal Bayesian Network, or Risk-Centric Causal Network
• Tutorials and Webinars — Multiple Hellixia artifacts