Using background knowledge makes FCI algorithm slower
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Description
Hi!
I would like to use the FCI algorithm with background knowledge, but I have noticed that the computation speed with the background is much slower than the computation speed without passing the background knowledge to FCI (or PC). I work with about 300 variables and there are not many dependencies (about 1,2 or 3) between them. In my background knowledge there are a lot of forbidden edges and some required edges. As far as I understand the FAS function, the amount of forbidden edges will reduce the adjacency list by a lot and also the separating set will be smaller than the version without the background knowledge.
I cannot find a explanation for the increase of computation speed, what am I missing?
Thanks in advance!
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Research direction
Start with the FAS function and the FCI and PC entry points mentioned in the report. Compare the background-knowledge and no-background cases on a sparse dataset with about 300 variables, focusing on forbidden and required edges; done means explaining or reproducing the observed slowdown.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100