The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups.

Imagine you're a researcher studying how sleep affects mood. To explore the connection, you would look at two key "variables": sleep (how much someone gets) and mood (how happy or irritable they feel) ...

An extraneous facet panel is displayed driven by data in additional layers if facet variable is not a factor. Is that by design ?