Exploring multi-treatment (discrete) cases
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Description
Hey, I'm getting started with CATE models in econml and I have been exploring examples for discrete treatments with p > 2. Is there in-built support for these, and how can I formulate such an example? For now I have explored passing treatment values as a list (eg. [1, 0, 0..]) to estimate effects on individual treatments, and other examples passing the entire treatment set. But estimating heterogenous effect at least in the _cate_estimator.py works for binary cases, so multi-treatment examples will require re-formulation as multi-binary cases. Is this observation correct?
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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 by reading econml/_cate_estimator.py and the existing discrete-treatment examples referenced in the issue. Determine whether treatments with p > 2 are supported and what formulation an example would require; done means providing a clear, validated answer or an agreed implementation scope.
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Assessment
- Tech stack
- jupyter-notebook, machine-learning, 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