But social science doesn't respect a correlation of .6 because they think it's a good way to measure something that could be measured directly. They find correlations either as an important step in establishing causation, a way to get large-scale trends, or a good way to measure something that can't be measured directly.

A correlation of 0.6 is a bad measurement, period. It does not become a good one for want of a better.

Can you or Richard give an example of something the people investigating lung cancer could have done with direct measurement that would have been more productive than analyzing the cigarettes-smoking correlation?

I don't know what you mean by "analysing" a correlation, but this is some of what they did do.

I could have mentioned epidemiology in my intro. The reason it depends on statistics is that it is often much more difficult to discern the actual mechanism of a disease process than to do statistical studies. Googling turns up this study which is claimed (by the scientist doing the work) to be the very first demonstration of a causal link between smoking and lung cancer -- in April of this year (and not the 1st of the month).

But the correlations remain what they are, and it still takes a lot of work to get somewhere with them.