Probabilistic causality is when A sometimes causes B, but not always. Like we mentioned in class, not everyone who smokes gets lung cancer, but smoking is still a cause. Another example would be that tanning causes skin cancer sometimes. Some would argue that this isn't real causation, because it doesn't happen every time. In response to that, a broader, better way to define probabilistic causality is that A causes B to be more likely to happen.
Another kind of situation that probabilistic causalities are useful in is when A, B, and C occur in succession, but A isn't necessarily the reason for B and/or C. An example given by the Stanford U site says that if barometric pressure drops (A), then there are two effects: the mercury drops in a barometer (B), and a storm occurs (C). Although chronologically, the events are A-->B-->C, B (the mercury drop) didn't cause C (the storm). In this case, A had two independent but related effects: it caused B for sure, and made C more likely. This is an example of a spurious regularity, which probabilistic causality helps out with.
http://www.everydaysociologyblog.com/2008/07/types-of-causal.html
http://plato.stanford.edu/entries/causation-probabilistic/
I think people are often confused with these causes and the words 'more likely' to get something than others. People either associate it with 'might not' or 'totally will' get it.
ReplyDeleteThe risk of everything is sometimes not a deterrent, especially for smokers and other habits like that. They often think 'increased' yes. 'But not me'.
I'm not sure anyone can clarify the risks to be persuasive enough. I can understand why this cause is problematic, as well as probabilistic!