evalITR
Evaluating Individualized Treatment Rules
Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019)
- GitHub
- https://michaellli.github.io/evalITR/
- https://jialul.github.io/causal-ml/
- File a bug report
- evalITR results
- evalITR.pdf
- Version1.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- Last release08/25/2023
Documentation
- VignetteCross-validation with multiple ML algorithms
- VignetteCross-validation with single algorithm
- VignetteInstallation
- Vignettepaper_alg1
- VignetteSample Splitting
- VignetteSample Splitting with Caret/SuperLearner
- VignetteUser Defined ITR
- VignetteCompare Estimated and User Defined ITR
- MaterialREADME
- MaterialNEWS
- In ViewsCausalInference
Team
Michael Lingzhi Li
Kosuke Imai
Show author detailsRolesAuthorJialu Li
Show author detailsRolesContributorXiaolong Yang
Show author detailsRolesContributor
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- Depends5 packages
- Imports19 packages
- Suggests9 packages