CausalForestDML.score performance on DML model selection
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
I have tried some CausalForestDML model and other DML model, I expect that model with better score shall be better, and the predicted ATE will be closer to ground truth ATE.
Here are some results:
How can I use it on model selection? and if there are any method to evaluate if model is good enough?
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
Start by reviewing the reported CausalForestDML.score results and the attached comparison. Establish whether the score is intended for DML model selection and what evidence would show that it tracks ATE accuracy; no files or tests are named, and completion would require resolving the evaluation guidance or behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100