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 add the optimal
CBPS which chooses the optimal balancing function and results in doubly robust
and efficient estimator for the treatment effect.
Christian Fong <christianfong@stanford.edu>,
Marc Ratkovic <ratkovic@princeton.edu>,
Chad Hazlett <chazlett@ucla.edu>,
Xiaolin Yang <xiaoliny@princeton.edu>,
Kosuke Imai <kimai@princeton.edu>