causalweight: Causal Inference Based on Inverse Probability Weighting, Doubly Robust Estimation, and Double Machine Learning

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>
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:, r-release:, r-oldrel:
macOS binaries: r-release: causalweight_0.2.1.tgz, r-oldrel: causalweight_0.2.1.tgz
Old sources: causalweight archive


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