
Source: Causality: Models, Reasoning, and Inference, 2000, p. 77 : cited in: Rick H. Hoyle (2014). Handbook of Structural Equation Modeling. p. 75
Hoyle (2014) further explained: In words, the functional details of M1 and M2 do not matter; what matters is that the assumptions in A (e.g. those encoded in the diagram) would constrain the variability of those details in such a way that equality of P's would entail equality of Q's. When this happens, Q depends on P only and should therefore be expressible in terms of the parameters of P. The section “Identification Using Graphs” will exemplify and operationalize this notion.