Multiple treatments when using grf.CausalForest
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Description
Hi,
This is more of a question concerning the grf module rather than an issue. We tried to use grf.CausalForest to estimate the heterogeneous causal effect with multiple treatments. In our case, the treatments are the coupons with different amount sent to customers, then should the parameter T be a one-hot encoding matrix, or just an array with the coupon amount?
Thx!
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by checking the documentation and examples for grf.CausalForest, focusing on the treatment parameter T and how multiple treatments are represented. Done means resolving whether T accepts one-hot treatment indicators or a numeric coupon amount and recording that guidance in the relevant documentation.
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Assessment
- Tech stack
- machine-learning, 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