Why do we need identification in causal inference? I am reading Pearl's causality book and it states,

Identifiability ensures that the added assumptions conveyed by $M$ ... will supply the missing information without explicating $M$ in detail.

However, I still do not understand the requirement for causal identification and would appreciate some elaboration of the above statement.

Let's say you have a Treatment variable, an Outcome variable and numerous other variables. One could do a regression of one on the other, adjusting by everything else, but we're smarter than this, right? How do we know this measures the direct relationship between treatment and outcome? Maybe we're not adjusting for an important confounder. Maybe we're adjusting for a collider and worsening our estimate, instead of what we really want.
The causal identification step is important to see if it's possible to estimate the effect of Treatment on Outcome. And if it is, how we can do so (backdoor adjustment, frontdoor adjustment, and so on). Sometimes it is not identifiable, and there is nothing we can do :|. Once the identification step is done, you can estimate the causal effect.
