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:Cyrill Scheidegger [aut, cre, cph]

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

On CRAN:

Conda:

3.30 score 2 stars 3 scripts 343 downloads 6 exports 10 dependencies

Last updated from:53fec825b5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK152
source / vignettesOK181
linux-releaseOK113
macos-releaseOK165
macos-oldrelOK226
windows-develOK67
windows-releaseOK77
windows-oldrelOK85
wasm-releaseOK84

Exports:bandwidth_normalfit_IVDMLrobust_confintrobust_p_value_aggregatedsestandard_confint

Dependencies:data.tablejsonlitelatticeMatrixmgcvnlmerangerRcppRcppEigenxgboost