Recommended reading "Proofiness: The Dark Arts of Mathematical Deception" by Seife. Talks about this fallacy and others in depth.
The best approach is multi-variate analysis. Which is to say for each correlation you find, identify another correlation that would be true (and is measurable) if the cause was what you hypothesize is the cause. Its a great way to write a paper too.
You start with "look at this correlation", we hypothesize that cause is "Q" and now we go look at the following correlations to prove or disprove our hypothesis, ... analysis and graphs ..., as you can see our hypothesis is thoroughly {proven | disproved} by these correlations.
If the data isn't available to do additional analysis then you are stuck trying to collect that data somehow. You can end up with inconclusive results in that case.
The best approach is multi-variate analysis. Which is to say for each correlation you find, identify another correlation that would be true (and is measurable) if the cause was what you hypothesize is the cause. Its a great way to write a paper too.
You start with "look at this correlation", we hypothesize that cause is "Q" and now we go look at the following correlations to prove or disprove our hypothesis, ... analysis and graphs ..., as you can see our hypothesis is thoroughly {proven | disproved} by these correlations.
If the data isn't available to do additional analysis then you are stuck trying to collect that data somehow. You can end up with inconclusive results in that case.