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" <arXiv:2203.12808> .

Version: 1.0.0
Depends: R (≥ 4.0.0)
Imports: xgboost, Rfast, stats, ranger, parallel
Suggests: fda, MASS, testthat (≥ 3.0.0), withr
Published: 2022-09-19
Author: David Carl ORCID iD [aut, cre], Corinne Emmenegger ORCID iD [aut], Wei Yuan [aut], Zijian Guo ORCID iD [aut]
Maintainer: David Carl <dcarl at ethz.ch>
License: GPL (≥ 3)
URL: https://github.com/dlcarl/TSML
NeedsCompilation: no
Citation: TSCI citation info
Materials: README
CRAN checks: TSCI results


Reference manual: TSCI.pdf


Package source: TSCI_1.0.0.tar.gz
Windows binaries: r-devel: TSCI_1.0.0.zip, r-release: TSCI_1.0.0.zip, r-oldrel: TSCI_1.0.0.zip
macOS binaries: r-release (arm64): TSCI_1.0.0.tgz, r-oldrel (arm64): TSCI_1.0.0.tgz, r-release (x86_64): TSCI_1.0.0.tgz, r-oldrel (x86_64): TSCI_1.0.0.tgz


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