pymc-labs / pymc-labs/CausalPy
Add uncertainty for the traditional (non-Bayesian) models
Open
Nobody has claimed this yet.
enhancement
wontfix
- Dominant language
- Python
- Stars
- 1.2k
- Forks
- 115
- Avg merge
- 6d 1h
- Merged PRs (30d)
- 11
Description
Suggested by @juanitorduz. Would be good to get measures of uncertainty for the non-Bayesian models. Could use:
Statsmodels notes
- Using R-style formulas https://www.statsmodels.org/dev/example_formulas.html (also see alternate constructor)
- Out of sample prediction: https://www.statsmodels.org/dev/examples/notebooks/generated/predict.html
Contributor guide
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
The issue names no implementation files or tests. Start by locating CausalPy's traditional non-Bayesian model implementations, then review the linked MAPIE and statsmodels references to define a supported uncertainty approach. Done means uncertainty measures are available for the intended non-Bayesian models and are covered by tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100