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The word "cause" (or "causation") has multiple meanings in English.In philosophical terminology, "cause" can refer to necessary, sufficient, or contributing causes. In examining correlation, "cause" is most often used to mean "one contributing cause" (but not necessarily the only contributing cause).
The presence of x, however, does not imply that y will occur. [20] Sufficient causes If x is a sufficient cause of y, then the presence of x necessarily implies the subsequent occurrence of y. However, another cause z may alternatively cause y. Thus the presence of y does not imply the prior occurrence of x. [20] Contributory causes
The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research.
The conventional dictum that "correlation does not imply causation" means that correlation cannot be used by itself to infer a causal relationship between the variables. [22] This dictum should not be taken to mean that correlations cannot indicate the potential existence of causal relations.
Pluralized causal principle - there are pluralized versions of universal causation, that allow exceptions to the principle. Robert K. Meyer's causal chain principle, [15] uses set theory axioms, assumes that something must cause itself in set of causes and so universal causation doesn't exclude self-causation. Against infinite regress.
Notably, correlation does not imply causation, so the study of causality is as concerned with the study of potential causal mechanisms as it is with variation amongst the data. [ citation needed ] A frequently sought after standard of causal inference is an experiment wherein treatment is randomly assigned but all other confounding factors are ...
Cause and effect may also be understood probabilistically, via inferential statistics, where the distinction between correlation and causation is important. Just because two variables are correlated does not mean that one caused the other. For example, ice cream sales are correlated with the number of deaths due to drowning.
Whenever there is lightning, there is thunder. The thunder does not cause the lightning (since lightning causes thunder), but because lightning always comes with thunder, we say that thunder is necessary for lightning. (That is, in its formal sense, necessity doesn't imply causality.) Example 4