I've had in the past a number of questions asked of me relating to published papers in a number of areas where regressions (and related models, such as panel models or GLMs) are used on observational data (i.e. data not produced by controlled experiment, in many cases - but not always - data observed over time) but where no attempt to introduce instrumental variables is made.
I've made a number of criticisms in response (such as describing issues with bias when important variables may be missing) but since other people here will no doubt be vastly more knowledgeable than me on this topic, I figured I'd ask:
What are the major issues/consequences of trying to come to conclusions about relationships (particularly, but not limited to causal conclusions) in such situations?
Can anything useful be done with studies that fit such models in the absence of instruments?
What are some good references (books or papers) on the issues with such modelling (preferably with clear nontechnical motivation of the consequences, since usually the people that ask have a variety of backgrounds, some without much statistics) that people might refer to in critiquing a paper? Discussion of precautions/problems with instruments would be useful too.
(Basic references on instrumental variables are here, though if you have any to add there, that would be helpful too.)
Pointers to good practical examples of finding and use of instruments would be a bonus but isn't central to this question.
[I'll likely point others to any good answers here as such questions come to me. I may add one or two examples as I get them.]