causalweight: Estimation Methods for Causal Inference Based on Inverse
Probability Weighting and Doubly Robust Estimation
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: |
1.1.4 |
| Depends: |
R (≥ 3.5.0), ranger |
| Imports: |
mvtnorm, np, LARF, hdm, SuperLearner, glmnet, xgboost, e1071, fastDummies, grf, checkmate, sandwich |
| Published: |
2025-12-01 |
| DOI: |
10.32614/CRAN.package.causalweight |
| Author: |
Hugo Bodory  [aut,
cre],
Martin Huber
[aut],
Jannis Kueck
[aut] |
| Maintainer: |
Hugo Bodory <hugo.bodory at unisg.ch> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| In views: |
CausalInference |
| CRAN checks: |
causalweight results |
Documentation:
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