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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
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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 |
Reference manual: | IVDML.pdf |
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-devel (arm64): IVDML_1.0.0.tgz, r-release (arm64): IVDML_1.0.0.tgz, r-oldrel (arm64): IVDML_1.0.0.tgz, r-devel (x86_64): 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.