Please clarify how mixed data are to be represented in causal-learn.
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Description
This is a request for information. For the mixed data project, could you clarify how mixed data are to be represented in causal-learn? That is, how is one to know, programmatically, which columns are for discrete data and which for continuous data? This cannot be gleaned from an np array itself, since binary data, for instance, can be treated as either continuous (with values 0 and 1) or discrete, and ordinal discrete data may often be treated as either continuous or discrete as well.
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Research direction
No file, test, or entry point is named. Review the existing mixed-data documentation and the seven-comment thread first to determine the intended representation; done means documenting an unambiguous programmatic way to identify discrete and continuous columns.
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Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100