plot.CBPS {CBPS}R Documentation

Plotting Covariate Balancing Propensity Score Estimation


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, ...)



an object of class “CBPS” or “npCBPS”, usually, a result of a call to CBPS or npCBPS.


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


If set to FALSE, returns the imbalances used to construct the plot. Default is TRUE, which returns nothing.


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


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 multi-valued 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


Christian Fong, Marc Ratkovic, and Kosuke Imai.

See Also

CBPS, plot

[Package CBPS version 0.23 Index]