Features X and W in DML
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
Hi all,
In DML, what kind of features can we add into X and W?
Take price elasticity estimation as an example, is this correct that only features uncorrelated (orthogonal) with current price can be added into X and W? Is lagged price or lagged sales a valid feature? Will collinearity between lagged price and current price bias the result of elasticity estimation?
Please help me. 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 names DML features X and W and asks about price elasticity, lagged price, lagged sales, and collinearity, but provides no file, test, or entry point. First clarify whether the goal is documentation or an implementation change; done would require a project-specific explanation of valid features and how these cases affect the estimation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100