Balance Tables

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.

For more information about Balance Tables, see Wikipedia: Balance Tables.

Keep in Mind

• 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 should control for that attribute as a variable in the RHS of your model specification.
• Balance tables can only report numeric differences and are not suitable for string value comparisions

Implementations

Python

R

# Import Dependency
library("cobalt")

# Load Data
data(mtcars)

# Create Balance Table
bal.tab(treat ~ foreign, data = mtcars)
## When using rmarkdown your table will be recorded

Stata

* 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(`output_dir'\\bal_tab.xls, replace)

Also Consider

The World Bank's very useful ietoolkit for Stata has a very flexible command for creating balance tables, iebaltab. You can learn more about how to use it on their Wiki page on the command.