Suppose a scientist hypothesizes model GO shown in Fig. 1a with the goal of estimating the direct effect of a treatment x on an outcome y (structural coefficient λxy).

λxy is the strength of the causal relationship, if there is no causal relationship, it's zero.

By the single-door criterion (Pearl 2000), she verifies λxy is identifiable in GO and equal to the regression estimand Ryx.z,

Another investigator, however, is suspicious that no common causes (confounders) exist between z and x in GO. She proposes an alternative model GA (Fig. 1b) such that the bidirected edge z ↔ x is included to account for that possibility.

So one question is how wrong could one be using Ryx.z to estimate λxy if the true causal model was given by graph GA? which is an example of identification in sensitivity Analysis.