# PSweight: An R Package for Propensity Score Weighting Analysis

Propensity score weighting is an important tool for comparative effectiveness research. Besides the inverse probability of treatment weights (IPW), recent development has introduced a general class of balancing weights, corresponding to alternative target populations and estimands. In particular, the overlap weights (OW) lead to optimal covariate balance and estimation efficiency, and a target population of scientific and policy interest. We develop the R package PSweight to provide a comprehensive design and analysis platform for causal inference based on propensity score weighting. PSweight supports (i) a variety of balancing weights, (ii) binary and multiple treatments, (iii) simple and augmented weighting estimators, (iv) nuisance-adjusted sandwich variances, and (v) ratio estimands. PSweight also provides diagnostic tables and graphs for covariate balance assessment. We demonstrate the functionality of the package using a data example from the National Child Development Survey (NCDS), where we evaluate the causal effect of educational attainment on income.

Tianhui Zhou (Department of Biostatistics and Bioinformatics) , Guangyu Tong (Department of Biostatistics) , Fan Li (Department of Statistical Science) , Laine E. Thomas (Department of Biostatistics and Bioinformatics) , Fan Li (Department of Biostatistics)
2022-06-21

## Supplementary materials

### Reuse

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### Citation

Zhou, et al., "The R Journal: PSweight: An R Package for Propensity Score Weighting Analysis", The R Journal, 2022

BibTeX citation

@article{RJ-2022-011,
author = {Zhou, Tianhui and Tong, Guangyu and Li, Fan and Thomas, Laine E. and Li, Fan},
title = {The R Journal: PSweight: An R Package for Propensity Score Weighting Analysis},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-011},
doi = {10.32614/RJ-2022-011},
volume = {14},
issue = {1},
issn = {2073-4859},
pages = {282-300}
}