Orthogonal / Double Machine Learning: model_final last-stage residual regression's intercept as specified by "fit_intercept"?
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
Hi! Thanks again for developing this powerful package.
Can someone help us understand: when we should specify an intercept in a Double ML's model_final residual regression? When we should not include any intercept in the last-stage residual regression? And, why? Any theory that underpins the recommended practices? Thanks!
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
The issue does not name a file, test, or entry point. Start by locating the model_final residual regression and its fit_intercept handling, then review the relevant orthogonal and Double Machine Learning documentation; done means documenting when each setting is appropriate and the theory supporting the recommendation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100