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: 1.1.0
Depends: R (≥ 3.5.0), ranger
Imports: mvtnorm, np, LARF, hdm, SuperLearner, glmnet, xgboost, e1071, fastDummies, grf, checkmate
Published: 2024-01-24
DOI: 10.32614/CRAN.package.causalweight
Author: Hugo Bodory ORCID iD [aut, cre], Martin Huber ORCID iD [aut], Jannis Kueck ORCID iD [aut]
Maintainer: Hugo Bodory <hugo.bodory at>
License: MIT + file LICENSE
NeedsCompilation: no
In views: CausalInference
CRAN checks: causalweight results


Reference manual: causalweight.pdf


Package source: causalweight_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): causalweight_1.1.0.tgz, r-oldrel (arm64): causalweight_1.1.0.tgz, r-release (x86_64): causalweight_1.1.0.tgz, r-oldrel (x86_64): causalweight_1.1.0.tgz
Old sources: causalweight archive


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