Categorical but non-binary treatment
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
I got a scenario that have categorical but non-binary treatment (can up to five option). Does DML and its variances, or metalearner support such scenario? It seems DML assumes partial treatment effect which does not work for multi-class treatment.
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 files, tests, or entry points are named. Start by locating the DML and metalearner implementations and checking how treatment variables and variance calculations currently handle multiple categories. Done would require a documented decision and implementation or confirmation of support for treatments with up to five options, but the issue does not define the expected API or tests.
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
- 25/100