 Here one can infer the effect of \(T\) on \(S\) from the observational distribution by conditioning on \(M\). In such a case, we say that  (4)  identifies the causal effect of \(T\) on \(S\). More generally, identifiability is a relationship between a DAG G, probability distribution \(P\) and a causal quantity \(\r{Q}\), such that \(\r{Q}\) is identifiable if and only if it is uniquely determined by \(P\) given \(G\). By contrast, when there are unmeasured common cause(s) of \(S\) and \(T\), the probability distribution is compatible with any possible distribution for \(p(\r{S}\mid \do (\r{T}))\).