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.

In causal inference you can think about identifiability as the condition that permit to measure causal quantity from observed data. Among parametric models is the condition that permit to estimate causal parameters from regressional. Formally
$E[Y|do(X)] = E[Y|X] $
Can be consider as identifiability condition.
Identifiability condition is one key point of any causal model. In general if you do not have enough assumptions and/or data identification is not possible.
For examples about it read here:
In Berkson's paradox, is $\beta_1 = 0$ or $\ne 0$?
Infer one link of a causal structure, from observations
