plot.CBMSM

an object of class &#8220;CBMSM&#8221;.

Indices of the covariates to be plotted (excluding the
intercept). For example, if only the first two covariates from
<code>balance</code> are desired, set <code>covars</code> to 1:2. The default is
<code>NULL</code>, which plots all covariates.

covars

If set to <code>FALSE</code>, returns the absolute imbalance for
each treatment history pair before and after weighting. This helps the user
to create his or her own customized plot. Default is <code>TRUE</code>, which
returns nothing.

silent

If set to <code>TRUE</code>, returns a boxplot summarizing the
imbalance on the covariates instead of a point for each covariate. Useful
if there are many covariates.

boxplot

Additional arguments to be passed to plot.

Plots the absolute difference in standardized means before and after
weighting.

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.

plot.CBMSM: Plotting CBPS Estimation for Marginal Structural Models

Description

Usage

Arguments

Value

Details

See Also