causalcausationcause and effectcausecausal relationshipcausal relationshipscausescause-and-effectcausallycaused
Causality (also referred to as causation, or cause and effect) is what connects one process (the cause) with another process or state (the effect), where the first is partly responsible for the second, and the second is partly dependent on the first.wikipedia
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Causality is metaphysically prior to notions of time and space.
Topics of metaphysical investigation include existence, objects and their properties, space and time, cause and effect, and possibility.
Interpreting causation as a deterministic relation means that if A causes B, then A must always be followed by B.
It is the concept that events within a given paradigm are bound by causality in such a way that any state (of an object or event) is completely determined by prior states.
In the philosophical literature, the suggestion that causation is to be defined in terms of a counterfactual relation is made by the 18th-century Scottish philosopher David Hume. Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes.
In what is sometimes referred to as Hume's problem of induction, he argued that inductive reasoning and belief in causality cannot be justified rationally; instead, our trust in causality and induction result from custom and mental habit, and are attributable only to the experience of "constant conjunction" of events.
Alternative methods of structure learning search through the many possible causal structures among the variables, and remove ones which are strongly incompatible with the observed correlations.
In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data.
confounding factorconfounding variableconfounding variables
One very practical result of this theory is the characterization of confounding variables, namely, a sufficient set of variables that, if adjusted for, would yield the correct causal effect between variables of interest.
Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.
In general this leaves a set of possible causal relations, which should then be tested by analyzing time series data or, preferably, designing appropriately controlled experiments.
Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated.
causationcorrelationcorrelation implies causation
A mere observation of a correlation is not nearly adequate to establish causality.
In statistics, many statistical tests calculate correlations between variables and when two variables are found to be correlated, it is tempting to assume that this shows that one variable causes the other.
Causality is metaphysically prior to notions of time and space.
The light cone has an essential role within the concept of causality.
degree of causalityGrangergranger causality analysis
This can be determined by statistical time series models, for instance, or with a statistical test based on the idea of Granger causality, or by direct experimental manipulation.
Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series.
Attribution theory is the theory concerning how people explain individual occurrences of causation.
A classical example of the distinction between "theoretical" and "practical" uses the discipline of medicine: medical theory involves trying to understand the causes and nature of health and sickness, while the practical side of medicine is trying to make people healthy.
Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes.
In his doctrine of transcendental idealism, he argued that space, time and causation are mere sensibilities; "things-in-themselves" exist, but their nature is unknowable.
See Causal Reasoning (Psychology) for more information.
Causal reasoning is the process of identifying causality: the relationship between a cause and its effect.
A. B. Hill's criteriaBradford Hill" criteriaHill criteria
(See Bradford-Hill criteria.) He did not note however, that temporality is the only necessary criterion among those aspects.
They 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.
regressionmultiple regressionregression model
The body of statistical techniques involves substantial use of regression analysis.
In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables.
A causal system is a system with output and internal states that depends only on the current and previous input values.
The idea that the output of a function at any time depends only on past and present values of input is defined by the property commonly referred to as causality.
David LewisLewis, DavidLewis
In his 1973 paper "Causation," David Lewis proposed the following definition of the notion of causal dependence:
These papers discuss his counterfactual theory of causation, the concept of semantic score, a contextualist analysis of knowledge, a dispositional value theory, among many other topics.
Within psychology, Patricia Cheng (1997) attempted to reconcile the Humean and Kantian views.
She is best known for her psychological work on human understanding of causality.
special theory of relativityrelativisticspecial
Otherwise, reference coordinate systems could be constructed (using the Lorentz transform of special relativity) in which an observer would see an effect precede its cause (i.e. the postulate of causality would be violated).
Therefore, if causality is to be preserved, one of the consequences of special relativity is that no information signal or material object can travel faster than light in vacuum.
John MackieJohn Leslie MackieMackie
J. L. Mackie argues that usual talk of "cause" in fact refers to INUS conditions (insufficient but non-redundant parts of a condition which is itself unnecessary but sufficient for the occurrence of the effect).
In metaphysics, Mackie made significant contributions relating to the nature of causal relationships, especially regarding conditional statements describing them (see, for example, Mackie 1974) and the notion of an INUS condition.
Statistics and economics usually employ pre-existing data or experimental data to infer causality by regression methods.
A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables.
Causality (also referred to as causation, or cause and effect) is what connects one process (the cause) with another process or state (the effect), where the first is partly responsible for the second, and the second is partly dependent on the first.
freedomfreewillfreedom of the will
The incompatibilist version of this holds that there is no such thing as "free will".
This includes interactionist dualism, which claims that some non-physical mind, will, or soul overrides physical causality.
cause-and-effect diagramfishbone diagramcause and effect diagram
For quality control in manufacturing in the 1960s, Kaoru Ishikawa developed a cause and effect diagram, known as an Ishikawa diagram or fishbone diagram.
Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa that show the causes of a specific event.
quantum physicsquantum mechanicalquantum theory
It is specifically characteristic of quantal phenomena that observations defined by incompatible variables always involve important intervention by the experimenter, as described quantitatively by the Heisenberg uncertainty principle.
Albert Einstein, himself one of the founders of quantum theory, did not accept some of the more philosophical or metaphysical interpretations of quantum mechanics, such as rejection of determinism and of causality.
Another sort of conditional, the counterfactual conditional, has a stronger connection with causality, yet even counterfactual statements are not all examples of causality.
The counterfactual conditional is the basis of experimental methods for establishing causality in the natural and social sciences, e.g., whether taking antibiotics helps cure bacterial infection.