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) <DOI:10.1214/17-AOAS1101>, 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.

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Description file content

Package
CBPS
Version
0.19
Date
2018-06-14
Title
Covariate Balancing Propensity Score
Depends
R (>= 3.4), MASS, MatchIt, nnet, numDeriv, glmnet
Imports
Description
Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . 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) , treatments with three- and four- valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2015) , 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.
LazyLoad
yes
LazyData
yes
License
GPL (>= 2)
NeedsCompilation
no
Repository
CRAN
RoxygenNote
6.0.1
Suggests
testthat
Packaged
2018-06-14 20:58:39 UTC; Christian
Author
Christian Fong [aut, cre], Marc Ratkovic [aut], Kosuke Imai [aut], Chad Hazlett [ctb], Xiaolin Yang [ctb], Sida Peng [ctb]
Maintainer
Christian Fong
Date/Publication
2018-06-17 06:07:07 UTC

install.packages('CBPS')

0.19

3 months ago

Christian Fong

GPL (>= 2)

Depends on

R (>= 3.4), MASS, MatchIt, nnet, numDeriv, glmnet

Suggests

testthat

Discussions