Balance Tables are a method by which you can statistically compare differences in characteristics between a treatment and control group. Common in experimental work and when using matching estimators, balance tables show if the treatment and control group are ‘balanced’ and can be seen as similarly ‘identical’ for comparison of a causal effect.
- When a characteristic is statistically different between control and treatment, your study is unbalanced in respect to that attribute.
- When a characteristic is unbalanced in your study, you may want to consider controlling for that attribute as a variable in your model specification.
- Balance tables can only report numeric differences and are not suitable for string value comparisions
# Import Dependency library("cobalt") # Load Data data(mtcars) # Create Balance Table bal.tab(am ~ mpg + hp, data = mtcars)
Another approach provides an omnibus summary of overall balance on many covariates and also allows for stratification like that arising from block-randomized experiments (for example those designed using the
blockTools package) or matched designs (for example using the
library(RItools) options(show.signif.stars=FALSE,digits=3) xb_res <- xBalance(am~mpg+hp+cyl+wt,strata=list(nostrat=NULL,vsstrat=~vs),data=mtcars,report="all") xb_res$overall xb_res$results[,c(1:3,6:7),]
* Import Dependency: 'ssc install table1' * Load Data sysuse auto, clear * Create Balance Table * You need to declare the kind of variable for each, as well as the variable by which you define treatment and control. * Adding test gives the statistical difference between the two groups. The ending saves your output as an .xls file table1, by(foreign) vars(price conts \ mpg conts \ weight contn \ length conts) test saving(bal_tab.xls, replace)