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Even when all three are satisfied, there may still not be a causal relationship since correlation may have been by way of coincidence. For example, if Google s
by alok-g 12y ago
Even when all three are satisfied, there may still not be a causal relationship since correlation may have been by way of coincidence. For example, if Google shows that the stock market change on a given day seems to correlate surprisingly well with search volume for a particular phrase on the day before, that may be just because the particular phrase was chosen from amongst a million others based on its correlation. Causation can still cannot be established (though a link cannot be denied either).
Note: If I recall correctly, Google did show one such study.
- srean 12y agoBut coincidences are covered by his (3). By definition coincidences are low probability events, if one does not account for coincidences, then yes, with low probability it will get it wrong. I think this gets more into the probably approximately correct (PAC) learning domain.
- alok-g 12y agoI was thinking also if they were covered by #3, but I am not sure: >> 3) A lack of other plausible explanations This means that there are not other parameters involved, satisfying the other two criteria, that can also plausibly "explain" the measured effect. In the example I gave, there may not be any such other available correlation. That the analyst picked the plausible cause by hunting though a large number of others is by itself not a plausible explanation asked for by #3.