I did a comparison of causal-learn to Tetrad on the task of analyzing the NASA Airfoil Self-Noise data (from the UCI repository) from a causal perspective. This is experimental data, so we know something of the ground truth, though not the full model. It presents various challenges to causal modeling; suggestions are made as to features to add to causal-learn (that are available currently in Tetrad) to help analyze it.
The document may evolve, so I give an Overleaf link that if successful will allow you to view the PDF. The PDF was created by Claude after a long discussion of how to analyze the data, with some features added to Tetrad specifically to analyze this and similar data.
https://www.overleaf.com/read/zvgdxhxgrptg#436652
This is in service of the goal of making it so public software can analyze real data successfully. I added the (causal-learn) FCI Fisher Z alpha 0.05 model recommended by causal-learn.com.
I did a comparison of causal-learn to Tetrad on the task of analyzing the NASA Airfoil Self-Noise data (from the UCI repository) from a causal perspective. This is experimental data, so we know something of the ground truth, though not the full model. It presents various challenges to causal modeling; suggestions are made as to features to add to causal-learn (that are available currently in Tetrad) to help analyze it.
The document may evolve, so I give an Overleaf link that if successful will allow you to view the PDF. The PDF was created by Claude after a long discussion of how to analyze the data, with some features added to Tetrad specifically to analyze this and similar data.
https://www.overleaf.com/read/zvgdxhxgrptg#436652
This is in service of the goal of making it so public software can analyze real data successfully. I added the (causal-learn) FCI Fisher Z alpha 0.05 model recommended by causal-learn.com.