Jeff Walker writes:
Your blog has skirted around the value of observational studies and chided folks for using causal language when they only have associations but I sense that you ultimately find value in these associations. I would love for you to expand this thought in a blog. Specifically:
Does a measured association “suggest” a causal relationship? Are measured associations a good and efficient way to narrow the field of things that should be studied? Of all the things we should pursue, should we start with the stuff that has some largish measured association? Certainly many associations are not directly causal but due to joint association. Similarly, there must be many variables that are directly causally associated ( A -> B) but the effect, measured as an association, is masked by confounders. So if we took the “measured associations are worthwhile” approach, we’d never or rarely find the masked effects. But I’d also like to know if one is more likely to find a large causal effect given some association, so the association makes a good “working hypothesis”. I hope I’ve asked these questions clearly enough. Effectively I’m asking, are observational studies worth the time and effort or would we be better to limit ourselves to experimental systems?
I like Don Rubin’s take on this, which is that if you want to go from association to causation, state very clearly what the assumptions are for this step to work. The clear statement of these assumptions can be helpful in moving forward (here’s an example from my own work, with Gary King).
Another way to say this is that all inference is about generalizing from sample to population, to predicting the outcomes of hypothetical interventions on new cases. You can’t escape the leap of generalization. Even a perfectly clean randomized experiment is typically of interest only to the extent that it generalizes to new people not included in the original study.