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OutcomeWeights: Outcome Weights of Treatment Effect Estimators

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: 0.1.1
Imports: ggplot2, grf, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2024-12-20
DOI: 10.32614/CRAN.package.OutcomeWeights
Author: Michael C. Knaus ORCID iD [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

Documentation:

Reference manual: OutcomeWeights.pdf

Downloads:

Package source: OutcomeWeights_0.1.1.tar.gz
Windows binaries: r-devel: OutcomeWeights_0.1.1.zip, r-release: OutcomeWeights_0.1.1.zip, r-oldrel: OutcomeWeights_0.1.0.zip
macOS binaries: r-release (arm64): OutcomeWeights_0.1.1.tgz, r-oldrel (arm64): OutcomeWeights_0.1.1.tgz, r-release (x86_64): OutcomeWeights_0.1.1.tgz, r-oldrel (x86_64): OutcomeWeights_0.1.1.tgz
Old sources: OutcomeWeights archive

Linking:

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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.