I only just came across the work of Judea Pearl (which shows how ignorant I am because he won the Turing Prize in 2011). I think his work is sensational and is essential reading for all scientists but in particular for social scientists and evaluators too.

Basically he says that science has suffered because statistics has failed to formally deal with causation, leaving it as a kind of mythical thing we only talk about in whispers. Pearl provides a robust and practical notation for causation with the do() operator and develops a complete set of theorems around it. In particular he shows under what conditions correlational data can indeed be used to draw causal conclusions. Plus he does it all with structural equation models which are fun to look at and easy to work with, (and which can be seen as the basis for the logframes and logic models which evaluators have to use every day).

Look at the second part of this annotated bibliography to see the sort of things he has been dealing with. , e.g. Pearl J. and E. Bareinboim, “Transportability of causal and statistical relations: A formal approach,” Proceedings, AAAI-11, 2011. Reduces the classical problem of external validity to mathematical transformations in the do-calculus, and establishes conditions under which experimental results can be generalized to new environments in which only passive observation can be conducted.

Anyway, the book might seem really hard (I am working through it but very slowly) but I just discovered there is an Epilogue right at the back of the book which provides a great summary. You can read it in an hour or two and it will definitely change your life.