Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.
| Version: | 0.2.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mvtnorm, np, LARF, hdm, SuperLearner, glmnet, ranger, xgboost, e1071 |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-06-15 |
| Author: | Hugo Bodory and Martin Huber |
| Maintainer: | Hugo Bodory <hugo.bodory at unifr.ch> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | causalweight results |
| Reference manual: | causalweight.pdf |
| Vignettes: |
The causalweight Package |
| Package source: | causalweight_0.2.1.tar.gz |
| Windows binaries: | r-devel: causalweight_0.2.1.zip, r-release: causalweight_0.2.1.zip, r-oldrel: causalweight_0.2.1.zip |
| macOS binaries: | r-release: causalweight_0.2.1.tgz, r-oldrel: causalweight_0.2.1.tgz |
| Old sources: | causalweight archive |
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