PSweight: Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials

Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

Version: 1.1.8
Depends: R (≥ 3.5.0)
Imports: lme4, nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner
Suggests: knitr, rmarkdown
Published: 2022-10-18
Author: Tianhui Zhou [aut, cre], Guangyu Tong [aut], Fan Li [aut], Laine Thomas [aut], Fan Li [aut]
Maintainer: Tianhui Zhou <thuizhou at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: CausalInference
CRAN checks: PSweight results


Reference manual: PSweight.pdf
Vignettes: PSweight_vig


Package source: PSweight_1.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PSweight_1.1.8.tgz, r-oldrel (arm64): PSweight_1.1.8.tgz, r-release (x86_64): PSweight_1.1.8.tgz, r-oldrel (x86_64): PSweight_1.1.8.tgz
Old sources: PSweight archive

Reverse dependencies:

Reverse imports: causal.decomp, RCTrep


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