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Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object ...
Causality is the relationship between causes and effects. [ 1 ] [ 2 ] While causality is also a topic studied from the perspectives of philosophy and physics, it is operationalized so that causes of an event must be in the past light cone of the event and ultimately reducible to fundamental interactions .
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.
Temporal paradoxes fall into three broad groups: bootstrap paradoxes, consistency paradoxes, and Newcomb's paradox. [1] Bootstrap paradoxes violate causality by allowing future events to influence the past and cause themselves, or "bootstrapping", which derives from the idiom "pull oneself up by one's bootstraps."
Causality is actually the other way around, since some diseases, such as cancer, cause low cholesterol due to a myriad of factors, such as weight loss, and they also cause an increase in mortality. [6] This can also be seen in alcoholics. [citation needed] As alcoholics become diagnosed with cirrhosis of the liver, many quit drinking. However ...
Causality, within sociology, has been the subject of epistemological debates, particularly concerning the external validity of research findings; one factor driving the tenuous nature of causation within social research is the wide variety of potential "causes" that can be attributed to a particular phenomena.
Causal reasoning is the process of identifying causality: the relationship between a cause and its effect.The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...