In some sense I feel to agree to it. In some other I am in deep disagreement. Maybe we have gone from a correlation implies causality view to any correlations is not indication of causality. Economics has gone so far that Freaking economists are popular because are able to find examples in which causality is pointing in the opposite direction than correlations seem to imply.
I shed an invisible tear whenever I hear “correlation does not imply causation” which the otherwise excellent swivel (a website about correlations) emphasizes. Of course, there’s truth to it. It saddens me because:It’s dismissive. It is often used to dismiss data from which something can be learned. The life-saving notion that smoking causes lung cancer was almost entirely built on correlations. For too long, these correlations were dismissed. It’s misleading. In real life, nothing unfailingly implies causation. In my experience, every data set has more than one interpretation. To “imply” causation requires diverse approaches and correlations are often among them. It’s a missed opportunity — namely, an opportunity to make a more nuanced statement about what we can learn from the data. It’s dogmatic (see “Jane Jacobs on Scientific Method”). Some correlations, such as those from “natural experiments,” imply causation much more than others. I suspect it does more harm than good to lump all of them together.
My sense is that decision theory may be able to help here. What if the correlation between smoking and lung cancer is actually causal? If we are wrong, that is confounding factors are at play, we may have sent bankrupt a few cigarettes companies. But it the relation is causal, as it is, we may have saved a lot of people.
Experiments (in social sciences especially) should be left to case where benefit and costs of misinterpration balance themselves out.
