I work in finance, and there's a class of models that works something like this:
Market A -> Market B
Very simply, you theorise that market A predicts market B, and you build a model along the lines of "regress market B returns on A" and so you decide whether to buy or sell B depending on what A did. Pretty much as simple as you can get.
However, suppose the real dynamic is this:
C -> Market A
C -> Market B
In other words, the real driver is another effect causing both A and B to move together. Let's say there's also autocorrelation in C.
In this case, A and B would still look mutually informative. When A goes up, B goes up, and so on. Furthermore, because there's a tendency for C to continue in the same direction, not only will A and B move together, a regression of B's next period returns would also look like it's correlated to A, confusing the issue of whether A is driving B or C is driving B. Of course this will come to a head when C is not doing anything and you try to make sense of why B is not responding to the noise in A.
I'm looking for terms to describe this situation, papers, readings, and so forth.
I would think it's pretty common, as it looks a lot like the simple graphs you see when studying causality, but I never found a convenient handle for the situation.