Package: OutcomeWeights 0.1.0
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:
OutcomeWeights_0.1.0.tar.gz
OutcomeWeights_0.1.0.zip(r-4.5)OutcomeWeights_0.1.0.zip(r-4.4)OutcomeWeights_0.1.0.zip(r-4.3)
OutcomeWeights_0.1.0.tgz(r-4.4-x86_64)OutcomeWeights_0.1.0.tgz(r-4.4-arm64)OutcomeWeights_0.1.0.tgz(r-4.3-x86_64)OutcomeWeights_0.1.0.tgz(r-4.3-arm64)
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://mcknaus.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mcknaus/outcomeweights/issues
Last updated 3 days agofrom:91f1fc8545. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 22 2024 |
| R-4.5-win-x86_64 | OK | Nov 22 2024 |
| R-4.5-linux-x86_64 | OK | Nov 22 2024 |
| R-4.4-win-x86_64 | OK | Nov 22 2024 |
| R-4.4-mac-x86_64 | OK | Nov 22 2024 |
| R-4.4-mac-aarch64 | OK | Nov 22 2024 |
| R-4.3-win-x86_64 | OK | Nov 22 2024 |
| R-4.3-mac-x86_64 | OK | Nov 22 2024 |
| R-4.3-mac-aarch64 | OK | Nov 22 2024 |
Exports:dml_with_smootherget_outcome_weightsNuPa_honest_forestpive_weight_makerprep_cf_matstandardized_mean_differences
Dependencies:clicolorspaceDiceKrigingfansifarverggplot2gluegrfgtableisobandlabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppEigenrlangsandwichscalestibbleutf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Double ML estimators with outcome smoothers | dml_with_smoother |
| Outcome weights method | get_outcome_weights |
| Outcome weights for the 'causal_forest' function | get_outcome_weights.causal_forest |
| Outcome weights for the 'dml_with_smoother' function | get_outcome_weights.dml_with_smoother |
| Outcome weights for the 'instrumental_forest' function | get_outcome_weights.instrumental_forest |
| Nuisance parameter estimation via honest random forest | NuPa_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 |
