Causality Analysis

Causality Analysis


To this day, no reliable methods exist to find causal relationships in data. Given a statistical association between two variables, it is impossible, based on data alone, to establish which variable is the cause and which is the effect.

As a result, acquiring additional external information, such as human expert knowledge or the temporal order of the variables, remains necessary to determine the causal direction in bivariate relationships.

With ChatGPT, it is now possible to let BayesiaLab tap into external domain knowledge. BayesiaLab's Hellixia can ask ChatGPT about the causal relationship between two nodes.

Usage & Example

  • Select two nodes of interest, e.g., Smoking and Lung Cancer.
  • Select Menu > Hellixia > Causality Search
  • In the Causality Search Window:
    • Specify the Completion Model.
    • Provide any applicable context to the Context field.
    • Check which fields contain the subjects under study, e.g., Node Name, Node Long Name, and Node Comment.
  • Click OK to launch the search.
  • If ChatGPT believes a causal relationship exists, BayesiaLab adds a corresponding arc.
  • Furthermore, BayesiaLab adds an Arc Comment with any contextual information ChatGPT provides. The Arc Comment icon indicates that such a comment was added.
  • Clicking the Show Arc Comment button in the Toolbar displays the comment.

Workflow Video

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