Obviously, it’s highly speculative at this point, but what would you guess the correlation coefficient is between cumulative welfare and biological ageing? How large does the correlation need to be before it’s useful?
We’d need to weight the different exposures by how widespread and frequent they are: some potential exceptions (e.g. food or reproduction) would be much more important than others (e.g. addictive drugs). Given some sort of weighted measure of this kind, I’d guess a moderate negative correlation, with a pretty wide uncertainty. I’d be quite surprised if it turned out to be near-zero or positive, though.
I think a moderate correlation like this should definitely be enough to be useful in many or most cases, given a sufficiently large sample. However, it also depends what exposure you’re actually interested in studying; if it turns out to be one of the exceptions then it doesn’t really matter how rare those exceptions are. So I think a better idea of where exactly exceptions to the rule might lie in the space of potential experiences would be more useful than estimating the overall correlation.
Given all that...
We’d need to weight the different exposures by how widespread and frequent they are: some potential exceptions (e.g. food or reproduction) would be much more important than others (e.g. addictive drugs). Given some sort of weighted measure of this kind, I’d guess a moderate negative correlation, with a pretty wide uncertainty. I’d be quite surprised if it turned out to be near-zero or positive, though.
I think a moderate correlation like this should definitely be enough to be useful in many or most cases, given a sufficiently large sample. However, it also depends what exposure you’re actually interested in studying; if it turns out to be one of the exceptions then it doesn’t really matter how rare those exceptions are. So I think a better idea of where exactly exceptions to the rule might lie in the space of potential experiences would be more useful than estimating the overall correlation.