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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-011.zip
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For attribution, please cite this work as
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}
}