Post-hoc fallacy is that condition where regression shows a high degree of correlation (R square is high), but there is no real cause-effect relationship between the independent and dependent variables

Post-hoc fallacy is that condition where regression shows a high degree of correlation (R square is high), but there is no real cause-effect relationship between the independent and dependent variables. For example, when the rooster crows each morning, the sun pops up every time (even if we can't see it). R square would be 1.00 in this case, but does that mean the rooster's crow actually caused the sun to rise? Clearly not, that would be post-hoc fallacy to conclude otherwise...there is no real cause and effect.

After doing a regression analysis on two sets of data, how would you go about ensuring that you have not fallen into the post-hoc fallacy trap?

How would you explain post-hoc fallacy to your boss or CO, who has no statistical background?