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In other words, when some factor is necessary to cause an effect, it is impossible to have the effect without the cause. [3] X would instead be a sufficient cause of y when the occurrence of x implies that y must then occur. [2] in other words, when some factor is sufficient to cause an effect, the presence of the cause guarantees the ...
In the field of epidemiology, the causal mechanisms responsible for diseases can be understood using the causal pie model.This conceptual model was introduced by Ken Rothman to communicate how constellations of component causes can lead to a sufficient cause to lead to a condition of interest and that reflection on these sets could improve epidemiological study design.
A condition can be both necessary and sufficient. For example, at present, "today is the Fourth of July" is a necessary and sufficient condition for "today is Independence Day in the United States". Similarly, a necessary and sufficient condition for invertibility of a matrix M is that M has a nonzero determinant.
Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between exposures to molds and infant pulmonary hemorrhage, [14] ultraviolet B radiation, vitamin D and cancer, [15] [16] vitamin D and pregnancy and neonatal outcomes, [17] alcohol and cardiovascular ...
A component cause is a factor that, along with other component causes, forms a sufficient cause for a disease. A sufficient cause is a complete combination of component causes necessary for the disease to manifest. Diseases result from a chain of causally related events, starting from an initial event to the clinical appearance of the disease.
Necessary condition analysis has found applications in a wide range of research areas. Some notable applications include: Business and management: NCA is used to identify the essential factors that are necessary for the success of a business, such as effective leadership, customer satisfaction, or employee engagement.
Epidemiologists emphasize that the "one cause – one effect" understanding is a simplistic mis-belief. [53] Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes. [54] Causes can be distinguished as necessary, sufficient or probabilistic conditions.
Judea Pearl defines a causal model as an ordered triple ,, , where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U ...