What I said in London was that whilst it is true that correlation does not necessarily equate to causation, all causally related variables will be correlated. Thus correlation is always necessary (but not in and of itself sufficient) for establishing causation.
The claim that 'correlation does not equal causation' is therefore meaningless when used to counter the results of correlative studies in which specific causal inferences are being made, as the inferred pattern of causation necessarily supervenes upon correlation amongst variables. Whether the variables being considered are in actuality causally associated as per the inference is another matter entirely.
The correct critique of such findings therefore is from mediation, i.e. the idea that a given correlation might be spurious owing to the presence of 'hidden' variables that are generating the apparent correlation. A famous example is yam production and national IQ, which across countries correlate negatively. It would be wrong to say that yam production somehow inhibits IQ, as the association will in fact turn out to be mediated by something like temperature and latitude. These variables are in turn proxies for historical and ecological trends that make the sort of countries that yield fewer yams the sort of countries that are typically populated by higher ability people, and vice versa. The causation in this case is via additional variables, which cause the covariance between the two variables of interest, without there being a direct effect of one on the other.
Properly constructed multivariate models can use these patterns of mediation to infer the likelihood of causation going in one direction or another. Thus it is possible to actually test causal inference amongst a population of correlated variables. By far the best way of doing this is to compare the fits of models containing specific theoretically prescribed patterns of causal inference against (preferably many) alternative theoretically plausible models, in which alternative patterns of causation are inferred (Figueredo & Gorsuch, 2007).
Sir William Gemmell Cochran termed this “Fisher’s Dictum‟:
“About 20 years ago, when asked in a meeting what can be done in observational studies to clarify the step from association to causation, Sir Ronald Fisher replied; `Make your theories elaborate.' The reply puzzled me at first, since by Occam's razor, the advice usually given is to make theories as simple as is consistent with known data. What Sir Ronald meant, as subsequent discussion showed, was that when constructing a causal hypothesis one should envisage as many different consequences of its truth as possible, and plan observational studies to discover whether each of these consequences is found to hold. (Cochran, 1965, §5).
Cochran, W. G. (1965). The planning of observational studies of human populations
(with Discussion). Journal of the Royal Statistical Society. Series A, 128, 134–155.
Figueredo, A. J., & Gorsuch, R. L. (2007). Assortative mating in the jewel wasp. 2.
Sequential cononical analysis as an exploratory form of path analysis. Journal of
the Arizona-Nevada Academy of Science, 39, 59-64.