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CauseNet: Towards a causality graph extracted from the web

(causenet.org)
229 points geetee | 1 comments | 02 Sep 25 05:26 UTC | HN request time: 0.001s | source
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larodi ◴[02 Sep 25 10:29 UTC] No.45101187[source]▶
Why not use PROLOG then, is the essence of cause and effect in programming. And also can expound syllogisms.
replies(1): >>45101599 #
orobus ◴[02 Sep 25 11:26 UTC] No.45101599[source]▶
The conditional relation represented in prolog, and in any deductive system, is material implication (~PvQ), not causation. You can encode causal relationships with material implication but you’re still going to need to discover those causal relationships in the world somehow.
replies(2): >>45101789 #>>45109754 #
1. cubefox ◴[02 Sep 25 11:45 UTC] No.45101789[source]▶
Conditional statements don't really work because "if A, then B" means that A is sufficient for B, but "A causes B" doesn't imply that A is sufficient for B. E.g. in "Smoking causes cancer", where smoking is a partial cause for cancer, or cancer partially an effect of smoking.

"A causes B" usually implies that A and B are positively correlated, i.e. P(A and B) > P(A)×P(B), but even that isn't always the case, namely when there is some common cause which counteracts this correlation.

Thinking about this, it seems that if A causes B, the correlation between A and B is at least stronger than it would have been otherwise.

This counterfactual difference in correlation strength is plausibly the "causal strength" between A and B. Though it doesn't indicate the causal direction, as correlation is symmetric.