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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  [aut,
cre],
Corinne Emmenegger
[aut],
Wei Yuan [aut],
Mengchu Zheng [aut],
Zijian Guo [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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=TSCI
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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.
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