The purpose of this study was to demonstrate the consequences of analyzing sequentially caused relationships with models assuming equally proximate causation. Monte Carlo simulations of data with well defined causations were performed. The logistic modeling approach was strongly misleading if a distant causal factor was treated as a factor being equally distant to the outcome as a proximal causal factor. In contrast, simple pathway analysis was able to correctly identify the true causation. In causal pathways, the relative risk of an intermediate cause with respect to the outcome needs to have a certain magnitude for the effect of the distant variable to be passed on. The results further show that the true relative risk of the distant variable is not dependent on its baseline prevalence. In contrast, the prevalence of the intermediate variable must be small enough to carry the influence of the distant variable through the causal chain. Practical epidemiologic exploration of etiological factors is presently dominated by stepwise multiple regression. This type of exploration is not model free but is often intuitively based on the structural assumption of equal proximity of all potential factors to the outcome. Equal proximity, however, is not likely in many etiologies, especially not if the causal factors under consideration are of different quality, like psychological and biological factors. In cases of causal pathways with some factors more distant and others more proximal to the outcome, the former tend to be dismissed by equal proximity modeling. Upstream exploration of more distant etiological factors is hindered by endemic stepwise multiple regression modeling, treating all variables as being equal in proximity to the outcome.