Originally, I did not think that this post helped answer my question about the y-axis scale on conditional_smooths(), until I realized the part where @paul.buerkner says “conditional_effects also adds the intercept”, implying that conditional_smooths() leaves it out. In my example above, the model is guassian, so I couldn’t figure out why conditional_smooths() differed from conditional_effects(), since the identity link is used (conditional_smooths() also shows things on the link scale, which differs from conditional_effects()). If I add the intercept from the model to the conditional_smooths() plot, then both plots look essentially the same (at least just eye-balling it).
I guess this makes me wonder why I would use conditional_smooths() when I can use conditional_effects(), which seems easier to interpret…? Maybe it is more useful in the case when there are multiple s() terms in the model.