Confounding variable is a more confusing, less precise name for the same phenomenon.
Omitted variable bias tells it like it is. A bias in a regression coefficient that results from an incorrectly specified model.
Omitted variable bias tells it like it is. A bias in a regression coefficient that results from an incorrectly specified model.
I wonder why Wikipedia has separate entries for them:
The picture at the top of the confounding article gives it away. Those kinds of diagrams are common in "hierarchical Bayesian models" like LDA.
In the simple linear setting, they are the same thing. Teaching people the "confounding variable" concept in a general setting before teaching them about "omitted variable" in a linear setting is like teaching people about Riemannian manifolds before teaching them about vector spaces.
Correlation isn't a particularly useful concept outside of simple linear models.