Causal Structural Priors

Causal Structural Priors


With the Causality Analysis function, Hellixia allows you to retrieve domain knowledge from ChatGPT about a potential causal relationship between two nodes.

The Causal Structural Priors function extends this concept to more than two nodes.

Usage & Example

  • We illustrate the Causal Structural Priors workflow with the well-known "Visit Asia" example from the domain of lung diseases.
  • We have a synthetic dataset from this domain, which has already been imported into BayesiaLab.
  • So, our starting point is an unconnected network, as shown in the following screenshot:
  • In addition to the descriptive and self-explanatory node names, Comments are associated with each node, as indicated by the information icon .
  • For instance, the node Smoking has an associated Node Comment that says, "The patient is a regular smoker."
  • Our objective is to find the causal relationships between risk factors, conditions, symptoms, and diagnostic imaging.
  • However, we know that machine learning alone cannot discover the true causal structure of this domain.
  • We begin with machine learning the associations between all nodes anyway and use the Unsupervised EQ learning algorithm for that purpose.
  • This newly-learned Bayesian network features directed arcs, but they can clearly not be interpreted as causal, e.g., Smoking could not possibly be a cause of Age.
  • Applying the Genetic Grid layout highlights the implausibility of the arc directions.
  • Select Menu > View > Layout > Genetic Grid Layout > Top-Down Repartition.
  • Note that the algorithm keeps searching for a better layout until you stop the process by clicking the red button![]( (opens in a new tab)" />to the left of the Progress Bar.
  • In the past, we would have had to use any available domain knowledge from experts to correct the arc directions.
  • With Hellixia, however, we can tap into the domain knowledge available via ChatGPT.
  • So, select all arcs and then select Menu > Hellixia > Causal Structural Priors.
  • In the Causal Structural Priors window, you need to specify a number of items:
    • Under Completion Model, choose a model for which you have a subscription, e.g., GPT_35 or GPT_4.
    • You can specify a General Context of the problem domain. In this example, "Lung Diseases" would be appropriate.
    • Under Subject of the Query, check all fields that contain information regarding the subject matter. We have information in the Node Name and the Node Comment in the example.
  • Clicking OK starts the search for causal relationships via ChatGPT. The progress bar at the bottom of the Graph Panel shows the search status.
  • A chime marks the completion of the search.
  • Furthermore, the Structural Priors icon appears in the bottom-right corner of the Graph Panel.
  • To view the Structural Priors obtained from ChatGPT, you can click on the Structural Priors icon or select Graph Panel Contextual Menu > Edit Structural Priors.
  • This table displays the causal arc directions obtained from ChatGPT in the three left columns.
  • The reason for the arc orientation is provided in the Explanation column.
  • The final column, Check, indicates whether the causal direction matches " the current orientation or not .
  • Clicking Preview opens a window showing a simplified view of the causal arc directions proposed by ChatGPT.

Now, there are two ways to proceed, as illustrated in the following workflows 1 and 2.

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