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.

This is my understanding.  Correct me if I am wrong.
Suppose I am conducting a randomized clinical trial to investigate the difference in means between two treatments, A and B.  If I randomize everyone to treatment B with probability 1 then the population mean for treatment A and the difference in means are both unidentifiable.  There is no data available to estimate these population quantities.
Suppose I randomize subjects to treatment B with probability 1/2 and during the course of the trial some subjects switch to other therapies.  The causal treatment effect (difference in population means between treatment A and B) in the envisioned scenario where post baseline treatment switching does not occur is unidentifiable using the observed data.
In both examples I could use an unverifiable missing data assumption that does make the population treatment effect identifiable.
