KCI independence test for mixed data
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- Dominant language
- Python
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Description
We want to run FCI with the KCI independence test and have a couple of binary variables (vegetarianism, gender) and a couple of scaled discreet variables (like smoking, where 0 is not smoking, 1 smoking rarely, 4 smoking frequently) and a couple of normal numerical variables.
We found several old discussions on mixed data, the general advice was to check out Tetrad and that causal-learn does not really support mixed data yet. Have there been any updates on that? What would you suggest now to use on the data we described?
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the issue's references to FCI, the KCI independence test, mixed binary, discrete, and numerical data, and the prior guidance about Tetrad and causal-learn. Determine whether current causal-learn entry points support this data combination and define what implementation or documentation would be needed; done means a supported approach or a clearly scoped feature proposal.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100