plot.CBPS {CBPS} R Documentation

## Plotting Covariate Balancing Propensity Score Estimation

### Description

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.

### Usage

```## S3 method for class 'CBPS'
plot(x, covars = NULL, silent = TRUE, boxplot = FALSE,
...)
```

### Arguments

 `x` an object of class “CBPS” or “npCBPS”, usually, a result of a call to `CBPS` or `npCBPS`. `covars` 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. `silent` If set to `FALSE`, returns the imbalances used to construct the plot. Default is `TRUE`, which returns nothing. `boxplot` 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.

### Details

The "Before Weighting" plot gives the balance before weighting, and the "After Weighting" plot gives the balance after weighting.

### @aliases plot.CBPS plot.npCBPS

### Value

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 [∑ w_i * (T_i == 1) * X_i]/[∑ w_i * (T_i == 1)] - [∑ w_i * (T_i == 0) * X_i]/[∑ w_i * (T_i == 0)]. For continuous treatments, plots the weighted absolute Pearson correlation between the treatment and each covariate. See https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#Weighted_correlation_coefficient.

### Author(s)

Christian Fong, Marc Ratkovic, and Kosuke Imai.