Comments (2)
mdagost
commented on July 24, 2024
WTF are these?! Or did you just make this name up? :)
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wlattner
commented on July 24, 2024
Nope, came across it a few weeks ago in this paper:
Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2017). Some methods for heterogeneous treatment effect estimation in high-dimensions. arXiv preprint arXiv:1707.00102.
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Related Issues (20)
- Superlearner
- Function(s) to build and compare several models on same training data
- Wrappers for popular estimators HOT 1
- Expose all estimators used in `hete_x` HOT 2
- Vignette walking through an analysis of the Gerber Green GOTV data HOT 1
- Consistent naming HOT 4
- Model calibration
- Setup Travis CI
- Sample weights
- Implement broom methods
- Differentiate between binary and continuous outcomes HOT 1
- Uplift should be cumulative
- Turn strata slides into narrative/vignette
- predict(model, newdata = NULL) should return predictions for training data
- hete_tot breaks when the outcome is a factor
- The uplift curve still looks wrong HOT 1
- Implement flip trick
- Set a reasonable default estimator
- hete_single example has wrong parameter order for uplift
- Try `parsnip` package for consistent interface to ml models
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