Question: Why the effect_inference produces a std_err for each point_estimate?
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
I am probably missing something more trivial here, but why is there a standard error associated with each point_estimate or CATE estimate in the EconML methods? I am looking at the Doubly Robust Learning method.
In general ML predictions, we determine standard error on the predicted data, but here there is a standard error associated with each prediction. What am I missing here?
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 Doubly Robust Learning material and the effect_inference behavior named in the issue. Trace how EconML presents CATE or point-estimate uncertainty, then document why a standard error can accompany each prediction and how that differs from standard ML prediction uncertainty.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100