The post suggests more, though doesn't illustrate it all (because I couldn't think of a simple way to do so). The key is that x and y can be statistically related yet still have zero correlation (because that is just one specific measure of relatedness). Interpretations of causation are not data driven. They come from theory.

The role of confounded factors came up in the examples because that is just an easy way to illustrate the points.