In addition to being thought of as a form of multiple regression focusing on causality, path analysis can be viewed as a special case of structural equation modeling (SEM) – one in which only single indicators are employed for each of the variables in the causal model.
Path analysis is considered by Judea Pearl to be a direct ancestor to the techniques of causal inference.
[2][3] It has since been applied to a vast array of complex modeling areas, including biology,[4] psychology, sociology, and econometrics.
[5] Typically, path models consist of independent and dependent variables depicted graphically by boxes or rectangles.
Graphically, these exogenous variable boxes lie at outside edges of the model and have only single-headed arrows exiting from them.
In this case, in addition to the three rules above, calculate expected covariances by: Where residual variances are not explicitly included, or as a more general solution, at any change of direction encountered in a route (except for at two-way arrows), include the variance of the variable at the point of change.