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>.

Version: 1.0.0
Imports: mgcv, ranger, stats, xgboost
Suggests: testthat (≥ 3.0.0)
Published: 2025-03-11
DOI: 10.32614/CRAN.package.IVDML
Author: Cyrill Scheidegger ORCID iD [aut, cre, cph]
Maintainer: Cyrill Scheidegger <cyrill.scheidegger at stat.math.ethz.ch>
License: GPL (≥ 3)
URL: https://github.com/cyrillsch/IVDML
NeedsCompilation: no
Materials: README NEWS
CRAN checks: IVDML results

Documentation:

Reference manual: IVDML.pdf

Downloads:

Package source: IVDML_1.0.0.tar.gz
Windows binaries: r-devel: IVDML_1.0.0.zip, r-release: IVDML_1.0.0.zip, r-oldrel: IVDML_1.0.0.zip
macOS binaries: r-release (arm64): IVDML_1.0.0.tgz, r-oldrel (arm64): IVDML_1.0.0.tgz, r-release (x86_64): IVDML_1.0.0.tgz, r-oldrel (x86_64): IVDML_1.0.0.tgz

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