Observation, causality and causal explanation

In this essay a dialogue is established between the main epistemological theories about causality that concludes with the differentiation between causality and causal explanation. This distinction is of great importance if one considers that causality is an inaccessible feature of the nature to which science approaches by means of causal explanations, that is, based on models that allow the organization of empirical material and examine the degree of correspondence between the expected results, provided by the model, and observations. As a particular case, correlation models such as regressions, trail analysis, multilevel models, panel analysis and, more generally, structural linear equations, among others, are concretions of the conceptual thinking of those who design them (or write) and submit to falsification. Consequently, the conclusion is that causality does not emerge from the data nor is it a result that emerges from the statistical models.