causalweight: Estimation Methods for Causal Inference Based on Inverse Probability Weighting

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: 1.0.1
Depends: R (≥ 3.5.0), ranger
Imports: mvtnorm, np, LARF, hdm, SuperLearner, glmnet, xgboost, e1071, fastDummies
Suggests: knitr, rmarkdown
Published: 2021-04-16
Author: Hugo Bodory ORCID iD [aut, cre], Martin Huber ORCID iD [aut]
Maintainer: Hugo Bodory <hugo.bodory at unisg.ch>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: causalweight results

Downloads:

Reference manual: causalweight.pdf
Vignettes: The causalweight Package
Package source: causalweight_1.0.1.tar.gz
Windows binaries: r-devel: causalweight_1.0.1.zip, r-release: causalweight_1.0.0.zip, r-oldrel: causalweight_1.0.1.zip
macOS binaries: r-release: causalweight_1.0.1.tgz, r-oldrel: causalweight_1.0.1.tgz
Old sources: causalweight archive

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