 As a general rule, our graphical representation of this SEM will include only the endogenous variables \(\bV\), and we use \(\bPA(X_i)\) to denote the set of endogenous parents of \(X_i . U_i\) is sometimes called an error variable for \(X_i\): it is responsible for any difference between the actual value of \(X_i\) and the value predicted on the basis of \(\bPA(X_i)\) alone. We may think of \(U_i\) as encapsulating all of the causes of \(X_i\) that are not included in \(\bV\). The assumption that each endogenous variable has exactly one error variable is innocuous. If necessary, \(U_i\) can be a vector of variables. For example, if \(Y_1\), \(Y_2\), and \(Y_3\) are all causes of \(X_i\) that are not included in \(\bV\), we can let \(U_i = \langle Y_1, Y_2, Y_3\rangle\). Moreover, the error variables need not be distinct or independent from one another.