CBPS: Covariate Balancing Propensity Score

Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting. The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <doi:10.1080/01621459.2014.956872>, treatments with three- and four- valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2015) <http://imai.princeton.edu/research/files/CBGPS.pdf>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Recently we have added the optimal CBPS which chooses the optimal balancing function and results in doubly robust and efficient estimator for the treatment effect as well as high dimensional CBPS when a large number of covariates exist.

Version: 0.16
Depends: R (≥ 3.3.2), MASS, MatchIt, nnet, numDeriv, glmnet
Suggests: testthat
Published: 2017-12-19
Author: Christian Fong [aut, cre], Marc Ratkovic [aut], Kosuke Imai [aut], Chad Hazlett [ctb], Xiaolin Yang [ctb], Sida Peng [ctb]
Maintainer: Christian Fong <christianfong at stanford.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: CBPS results


Reference manual: CBPS.pdf
Package source: CBPS_0.16.tar.gz
Windows binaries: r-devel: CBPS_0.15.zip, r-release: CBPS_0.15.zip, r-oldrel: CBPS_0.15.zip
OS X El Capitan binaries: r-release: CBPS_0.15.tgz
OS X Mavericks binaries: r-oldrel: CBPS_0.15.tgz
Old sources: CBPS archive

Reverse dependencies:

Reverse suggests: cobalt, WeightIt
Reverse enhances: optmatch


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