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:	1.0.3
Depends:	R (≥ 3.5.0), ranger
Imports:	mvtnorm, np, LARF, hdm, SuperLearner, glmnet, xgboost, e1071, fastDummies
Published:	2022-08-12
Author:	Hugo Bodory [aut, cre], Martin Huber [aut]
Maintainer:	Hugo Bodory <hugo.bodory at unisg.ch>
License:	MIT + file LICENSE
NeedsCompilation:	no
In views:	CausalInference
CRAN checks:	causalweight results

Documentation:

Reference manual:

causalweight.pdf

Downloads:

Package source:	causalweight_1.0.3.tar.gz
Windows binaries:	r-devel: causalweight_1.0.3.zip, r-release: causalweight_1.0.3.zip, r-oldrel: causalweight_1.0.3.zip
macOS binaries:	r-release (arm64): causalweight_1.0.3.tgz, r-oldrel (arm64): causalweight_1.0.3.tgz, r-release (x86_64): causalweight_1.0.3.tgz, r-oldrel (x86_64): causalweight_1.0.3.tgz
Old sources:	causalweight archive

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