TDA causal discovery
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.7k
- Forks
- 274
- PR merge metrics
- No merged PRs in 30d
Description
Sounds like we should add TDA
Topological data analysis (TDA) is a field of mathematics that studies the topological properties of data sets. Topological properties are those that are preserved under continuous deformations, such as the number of connected components or the existence of holes. TDA has been used to solve a variety of problems in machine learning, including clustering, classification, and anomaly detection.
Contributor guide
No contributing guide indexed for this repository
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
No file, test, or entry point is named. First map how causal-learn exposes causal-discovery methods, then define the TDA scope and acceptance criteria before implementation and testing.
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