balance.CBPSContinuous

object

<p>Additional arguments to be passed to balance.</p>

<p>Calculates the pre- and post-weighting correlations between each covariate and the T</p>

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 optimal
CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,
several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
They include 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 (2018)
<DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of
covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, 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.

Christian Fong

CBPS

Covariate Balancing Propensity Score

Marc Ratkovic

Kosuke Imai

Chad Hazlett

Xiaolin Yang

Sida Peng

Inbeom Lee

balance.CBPSContinuous function

Calculates the pre- and post-weighting correlations between each covariate and the T — balance.CBPSContinuous

Calculates the pre- and post-weighting correlations between each covariate and the T

balance.CBPSContinuous: Calculates the pre- and post-weighting correlations between each covariate and the T

Description

Usage

Arguments