plot.CBPS {CBPS}  R Documentation 
This function plots the absolute difference in standardized means before and after
weighting. To access more sophisticated graphics for assessing covariate balance,
consider using Noah Greifer's cobalt
package.
## S3 method for class 'CBPS'
plot(x, covars = NULL, silent = TRUE, boxplot = FALSE, ...)
x 
an object of class “CBPS” or “npCBPS”, usually, a
result of a call to 
covars 
Indices of the covariates to be plotted (excluding the
intercept). For example, if only the first two covariates from

silent 
If set to 
boxplot 
If set to 
... 
Additional arguments to be passed to plot. 
The "Before Weighting" plot gives the balance before weighting, and the "After Weighting" plot gives the balance after weighting.
### @aliases plot.CBPS plot.npCBPS
For binary and multivalued treatments, plots the absolute
difference in standardized means by contrast for all covariates before and
after weighting. This quantity for a single covariate and a given pair of
treatment conditions is given by \frac{\sum_{i=1}^{n} w_i * (T_i == 1)
* X_i}{\sum_{i=1}^{n} (T_i == 1) * w_i}  \frac{\sum_{i=1}^{n} w_i * (T_i ==
0) * X_i}{\sum_{i=1}^{n} (T_i == 0) * w_i}
. For continuous treatments, plots the weighted absolute
Pearson correlation between the treatment and each covariate. See
https://en.wikipedia.org/wiki/Pearson_productmoment_correlation_coefficient#Weighted_correlation_coefficient.
Christian Fong, Marc Ratkovic, and Kosuke Imai.