A Confounding variable is an important variable that should be included in the predictive model but you omit it.Naive interpretation of such models can lead to invalid conclusions.
For example, consider that we want to model life expentency in different countries based on the GDP per capita, using the gapminder data set:
library(gapminder)
lm(lifeExp ~ gdpPercap, data = gapminder)
In this example, it is clear that the continent is an important variable: countries in Europe are estimated to have a higher life expectancy compared to countries in Africa. Therefore, continent is a confounding variable that should be included in the model:
lm(lifeExp ~ gdpPercap + continent, data = gapminder)
