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Many treatment effect estimators can be written as weighted outcomes. These weights have established use cases like checking covariate balancing via packages like 'cobalt'. This package takes the original estimator objects and outputs these outcome weights. It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>. This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.
Version: | 0.1.0 |
Imports: | ggplot2, grf, methods |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2024-11-21 |
Author: | Michael C. Knaus [aut, cre], Henri Pfleiderer [ctb] |
Maintainer: | Michael C. Knaus <michael.knaus at uni-tuebingen.de> |
BugReports: | https://github.com/MCKnaus/OutcomeWeights/issues |
License: | GPL-3 |
URL: | https://github.com/MCKnaus/OutcomeWeights |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | OutcomeWeights results |
Reference manual: | OutcomeWeights.pdf |
Package source: | OutcomeWeights_0.1.0.tar.gz |
Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
macOS binaries: | r-release (arm64): OutcomeWeights_0.1.0.tgz, r-oldrel (arm64): OutcomeWeights_0.1.0.tgz, r-release (x86_64): OutcomeWeights_0.1.0.tgz, r-oldrel (x86_64): OutcomeWeights_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.