 Causal models have found applications in signal processing, epidemiology and machine learning.Pearl defines a causal model as an ordered triple formula_1, where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U and V.

Aristotle defined a taxonomy of causality, including material, formal, efficient and final causes.Hume rejected Aristotle's taxonomy in favor of counterfactuals.