Different results with Tetrad and causal-learn implementations.
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Description
Dear developers,
I have been trying to use causal learn models with fMRI data. I used both the Tetrad implementation (using pyTetrad and JPype) and the causal-learn implementations.
What surprises me is that I'm getting quite different results from these implementations. Using the PC algorithms in a 100 variables graph (4950 potential undirected edges) the algorithms' results differ in 155 edges.
Also, the GES algorithm in causal-learn is orders of magnitude (hours vs seconds) slower than the Tetrad implementation.
Kind regards
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Research direction
The issue names no files, tests, dataset, or algorithm settings. Start by reproducing the PC and GES comparisons using the same inputs and options in causal-learn and the Tetrad implementation via pyTetrad and JPype. Done means identifying and documenting the source of the differing edges and performance, or narrowing it to a specific implementation discrepancy.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100