Monotonicity constraints for effect prediction
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
Hello again,
I have been trying to implement DML methods to predict the causal effect of a continuous treatment (from 0 to 300 aprox) and even if I got to the point of having a fairly good MSE score, almost half of the effects predicted are negative. I am an expert on the field from where the database is extracted, and it is impossible for this drug to have a negative effect. The effect is always positive, and increasing with higher quantities of treatment (up to a plateu in some patients).
My question is, is it possible to apply monotonicity constraints to the final model and in doing so specifying to the estimator that the treatment can only increase the effect?
Thank you in advance!
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 specific estimator entry point are named. Start by locating the DML estimator used for continuous treatments and determine whether monotonicity constraints are supported; done would require a defined approach and validation that predicted effects are nonnegative and increase with treatment.
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
- 20/100