How to use PolicyTree from a CausalForest to predict treatment of new unseen data?
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
@kbattocchi Hi Keith! I hope you had a great start into 2023. I wanted to ask you if I am able to use a Policy Tree and score new data on it? I.e. predict that is the optimal treatment for a given instance.
My model choice was a CausalForest BUT when I follow the "Policy Learning" section here https://github.com/microsoft/EconML it tells me that CausalForest does not have any predict(X) option. Is that true or is there any other trick? Does predict(X) ONLY come with a subset of learners such as DR, etc?
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 with the EconML "Policy Learning" section and the CausalForest API described in the issue. Determine whether a PolicyTree can score new unseen data from a CausalForest and whether predict(X) is limited to other learners; done means the supported workflow and any documentation gap are clearly identified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100