TSCI: Tools for Causal Inference with Possibly Invalid Instrumental Variables

Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2023) "TSCI: two stage curvature identification for causal inference with invalid instruments" <doi:10.48550/arXiv.2304.00513>.

Version: 3.0.4
Depends: R (≥ 4.0.0)
Imports: xgboost, Rfast, stats, ranger, parallel, fastDummies
Suggests: fda, MASS, testthat (≥ 3.0.0), withr
Published: 2023-10-09
Author: David Carl ORCID iD [aut, cre], Corinne Emmenegger ORCID iD [aut], Wei Yuan [aut], Mengchu Zheng [aut], Zijian Guo ORCID iD [aut]
Maintainer: David Carl <david.carl at phd.unibocconi.it>
License: GPL (≥ 3)
URL: https://github.com/dlcarl/TSCI
NeedsCompilation: no
Citation: TSCI citation info
Materials: README
CRAN checks: TSCI results

Documentation:

Reference manual: TSCI.pdf

Downloads:

Package source: TSCI_3.0.4.tar.gz
Windows binaries: r-devel: TSCI_3.0.4.zip, r-release: TSCI_3.0.4.zip, r-oldrel: TSCI_3.0.4.zip
macOS binaries: r-release (arm64): TSCI_3.0.4.tgz, r-oldrel (arm64): TSCI_3.0.4.tgz, r-release (x86_64): TSCI_3.0.4.tgz
Old sources: TSCI archive

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