In an interesting review essay, Andrew Gelman (Columbia) discusses the all-important move from describing what to assessing what if or why. Obviously, inference is more complex than description. Gelman considers two broad classes of inferential questions. One involves "forward causal inference." That is, what might happen if we do X? A second is "reverse causal inference," or what causes Y? Equally interesting are Gelman's thoughts on what characterizes persuasive inferential claims.
“The most compelling causal studies have (i) a simple structure that you can see through to the data and the phenomenon under study, (ii) no obvious plausible source of major bias, (iii) serious efforts to detect plausible biases, efforts that have come to naught, and (iv) insensitivity to small and moderate biases (see, e.g., Greenland 2005). Two large unresolved problems are, first, how to best achieve these four steps in practice and, second, what sorts of causal claims to make in settings where we are not able to satisfy these conditions.”