Search results
Results From The WOW.Com Content Network
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 (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. [1]
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.
Idappaccayatā (Pali, also idappaccayata; Sanskrit: idaṃpratyayatā) is a Buddhist term that is translated as "specific conditionality" or "this/that conditionality". It refers to the principle of causality: that all things arise and exist due to certain causes (or conditions), and cease once these causes (or conditions) are removed.
In the Scholasticism, the efficient causality [35] was governed by two principles: omne agens agit simile sibi [36] [37] [38] (every agent produces something similar to itself): stated frequently in the writings of St. Thomas Aquinas, the principle establishes a relationship of similarity and analogy between cause and effect;
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.
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.
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.
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.