This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables St and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions (...) in the systems to which T applies or the magnitudes of the conditional probabilities P(Mj/Si), in the manner in which Ir does. (shrink)

In a recent article [1], Kenneth Sayre presents what he takes to be an alternative to Salmon and Reichenbach's screening-off model of causal relations. His statistical model, based on communication theory, supposedly “has the added advantage of distinguishing cause and effect without reference to temporal distinction”. Unfortunately his model falls far short of this grand claim, faltering before the same sort of examples I had previously used to assail the screening-off model [2].