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 2Last updated