Package: OutcomeWeights 0.1.0

Michael C. Knaus

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.

Authors:Michael C. Knaus [aut, cre], Henri Pfleiderer [ctb]

OutcomeWeights_0.1.0.tar.gz
OutcomeWeights_0.1.0.tar.gz(r-4.5-noble)OutcomeWeights_0.1.0.tar.gz(r-4.4-noble)
OutcomeWeights_0.1.0.tgz(r-4.4-emscripten)OutcomeWeights_0.1.0.tgz(r-4.3-emscripten)
OutcomeWeights.pdf |OutcomeWeights.html
OutcomeWeights/json (API)
NEWS

# Install 'OutcomeWeights' in R:
install.packages('OutcomeWeights', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mcknaus/outcomeweights/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

1.70 score 6 exports 36 dependencies

Last updated 24 days agofrom:8e4047dc0d. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-linux-x86_64OKNov 22 2024

Exports:dml_with_smootherget_outcome_weightsNuPa_honest_forestpive_weight_makerprep_cf_matstandardized_mean_differences

Dependencies:clicolorspaceDiceKrigingfansifarverggplot2gluegrfgtableisobandlabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppEigenrlangsandwichscalestibbleutf8vctrsviridisLitewithrzoo