Causal Structural Priors

Context

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 Main 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 buttonto 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 Main 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.

pageWorkflow 1pageWorkflow 2