For starters in the world of machine learning and predictive analytics, it doesn't really matter if X causes Y so long as X is a consistently good predictor of Y. Maybe powerlines being over someone's home are not the cause of cancer, but if their presence can be used to predict cancer rates that's a good thing.
More important imho is the idea of latent or hidden variables. Two things that are clearly correlated but also seem to not have a causal relationship (just as transistors and longevity) may share a latent variable, that may be either non-quantifiable or completely unobservable. For either case measuring outputs that share a common latent variable and thus correlate with each other might be the only way to attempt to measure hidden, non-quantifiable causes.
For example employee happiness might be the cause of employee retention. However you can't currently measure or observe 'happiness', but there may be many, seemingly, unrelated employee activities that correlate with retention because they are also driven by this same latent variable. Studying them is the only way to get a quantifiable understanding of this latent cause.
tl;dr somethimes correlation is just as important as causation.
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