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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) <arXiv:1905.05389>.
Getting started
Browse package contents
Package details |
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| Author | Michael Lingzhi Li [aut, cre], Kosuke Imai [aut], Jialu Li [ctb], Xiaolong Yang [ctb] |
| Maintainer | Michael Lingzhi Li <mili@hbs.edu> |
| License | GPL (>= 2) |
| Version | 1.0.0 |
| URL | https://github.com/MichaelLLi/evalITR https://michaellli.github.io/evalITR/ https://jialul.github.io/causal-ml/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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