Package: IVDML 1.0.0

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_1.0.0.tar.gz
IVDML_1.0.0.zip(r-4.5)IVDML_1.0.0.zip(r-4.4)IVDML_1.0.0.zip(r-4.3)
IVDML_1.0.0.tgz(r-4.5-any)IVDML_1.0.0.tgz(r-4.4-any)IVDML_1.0.0.tgz(r-4.3-any)
IVDML_1.0.0.tar.gz(r-4.5-noble)IVDML_1.0.0.tar.gz(r-4.4-noble)
IVDML_1.0.0.tgz(r-4.4-emscripten)IVDML_1.0.0.tgz(r-4.3-emscripten)
IVDML.pdf |IVDML.html✨
IVDML/json (API)
NEWS

# 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:

3.18 score 1 stars 6 exports 10 dependencies

Last updated 4 days agofrom:e1ab9dfb30. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 12 2025
R-4.5-winOKMar 12 2025
R-4.5-macOKMar 12 2025
R-4.5-linuxOKMar 12 2025
R-4.4-winOKMar 12 2025
R-4.4-macOKMar 12 2025
R-4.4-linuxOKMar 12 2025
R-4.3-winOKMar 12 2025
R-4.3-macOKMar 12 2025

Exports:bandwidth_normalfit_IVDMLrobust_confintrobust_p_value_aggregatedsestandard_confint

Dependencies:data.tablejsonlitelatticeMatrixmgcvnlmerangerRcppRcppEigenxgboost