Corresponding correlations is a method that allows us to infer formal causation from correlational data. In this paper, causal terms are traced to their philosophical and etymological roots. It is argued that causes are parts of their mutual whole . Nominalism, normal distributions and disjunctive causes are linked. Causal manifolds and sampling by potential are used to model conjunctive causes. Corresponding correlations are then demonstrated through simulations, in which causal relations are d…
Read moreCorresponding correlations is a method that allows us to infer formal causation from correlational data. In this paper, causal terms are traced to their philosophical and etymological roots. It is argued that causes are parts of their mutual whole . Nominalism, normal distributions and disjunctive causes are linked. Causal manifolds and sampling by potential are used to model conjunctive causes. Corresponding correlations are then demonstrated through simulations, in which causal relations are differentiated from spurious correlations. An algebraic method for unraveling confounded variables is presented. Distinctions between laws and causes are made and related to corresponding correlations. The conclusion is that corresponding correlations should be a significant advance in causal inference
