Package: OutcomeWeights 0.2.0.9000

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_0.2.0.9000.tar.gz
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OutcomeWeights_0.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
OutcomeWeights/json (API)
NEWS

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

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

On CRAN:

Conda:

openblascppopenmp

5.03 score 4 stars 38 scripts 131 downloads 7 exports 27 dependencies

Last updated from:0c94f940b0. Checks:11 WARNING, 1 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING184
linux-devel-x86_64WARNING200
source / vignettesERROR229
linux-release-arm64WARNING193
linux-release-x86_64WARNING178
macos-release-arm64WARNING160
macos-release-x86_64WARNING298
macos-oldrel-arm64WARNING110
macos-oldrel-x86_64WARNING274
windows-develWARNING181
windows-releaseWARNING155
windows-oldrelWARNING173
wasm-releaseOK137

Exports:dml_with_smootherget_outcome_weightsget_smoother_weightsNuPa_honest_forestpive_weight_makerprep_cf_matstandardized_mean_differences

Dependencies:clicpp11DiceKrigingfarverggplot2gluegrfgtableisobandlabelinglatticelifecyclelmtestMatrixR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7sandwichscalesvctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Double ML estimators with outcome smoothersdml_with_smoother
Outcome weights methodget_outcome_weights
Outcome weights for the 'causal_forest' functionget_outcome_weights.causal_forest
Outcome weights for the 'dml_with_smoother' functionget_outcome_weights.dml_with_smoother
Outcome weights for a 'DoubleML' objectget_outcome_weights.DoubleML
Outcome weights for the 'instrumental_forest' functionget_outcome_weights.instrumental_forest
Outcome weights for ivregget_outcome_weights.ivreg
Outcome weights for the lm commandget_outcome_weights.lm
Outcome weights for the 'lm_robust' commandget_outcome_weights.lm_robust
Smoother weights methodget_smoother_weights
Smoother weights for the 'plasso::cv.plasso' functionget_smoother_weights.cv.plasso
Smoother weights for the 'drf::drf' functionget_smoother_weights.drf
Smoother weights for the 'stats::lm' functionget_smoother_weights.lm
Smoother weights for the 'estimatr::lm_robust' functionget_smoother_weights.lm_robust
Smoother weights for outcome modelsget_smoother_weights.NuisanceParameters
Smoother weights for the 'ranger::ranger' functionget_smoother_weights.ranger
Smoother weights for the 'grf::regression_forest' functionget_smoother_weights.regression_forest
Smoother weights for the 'hdm::rlasso' functionget_smoother_weights.rlasso
Smoother weights for the 'xgboost::xgb.train' functionget_smoother_weights.xgb.Booster
Nuisance parameter estimation via honest random forestNuPa_honest_forest
Outcome weights maker for pseudo-IV estimators.pive_weight_maker
'plot' method for class 'dml_with_smoother'plot.dml_with_smoother
Creates matrix of binary cross-fitting fold indicators (N x # cross-folds)prep_cf_mat
Calls C++ implementation to calculate standardized mean differences.standardized_mean_differences
'summary' method for class 'dml_with_smoother'summary.dml_with_smoother
'summary' method for class 'outcome_weights'summary.get_outcome_weights
'summary' method for class 'standardized_mean_differences'summary.standardized_mean_differences