Package: IVDML 1.0.2
IVDML: Double Machine Learning with Instrumental Variables and Heterogeneity
Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.
Authors:
IVDML_1.0.2.tar.gz
IVDML_1.0.2.zip(r-4.7-any)IVDML_1.0.2.zip(r-4.6-any)IVDML_1.0.2.zip(r-4.5-any)
IVDML_1.0.2.tgz(r-4.6-any)IVDML_1.0.2.tgz(r-4.5-any)
IVDML_1.0.2.tar.gz(r-4.7-any)IVDML_1.0.2.tar.gz(r-4.6-any)
IVDML_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
IVDML/json (API)
| # Install 'IVDML' in R: |
| install.packages('IVDML', repos = c('https://cyrillsch.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/cyrillsch/ivdml/issues
Last updated from:53fec825b5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 152 | ||
| source / vignettes | OK | 181 | ||
| linux-release | OK | 113 | ||
| macos-release | OK | 165 | ||
| macos-oldrel | OK | 226 | ||
| windows-devel | OK | 67 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 85 | ||
| wasm-release | OK | 84 |
Exports:bandwidth_normalfit_IVDMLrobust_confintrobust_p_value_aggregatedsestandard_confint
Dependencies:data.tablejsonlitelatticeMatrixmgcvnlmerangerRcppRcppEigenxgboost