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Что (кто) такое causal$551728$ - определение

APPROACH TO QUANTUM PHYSICS

Causal set; Causal Set; Causal Sets; Causet; Causal set theory; Causal set theory bibliography; User:Sumatisurya/sandbox; Causal Set Theory Bibliography; Causal set bibliography

Dynamic causal modeling

FMRI ANALYSIS METHOD

Dynamic causal modelling; User:Peterzlondon/sandbox; Draft:Dynamic causal modeling

Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations.

https://en.wikipedia.org/wiki/Dynamic_causal_modeling

Causal reasoning

PROCESS OF IDENTIFYING CAUSALITY: THE RELATIONSHIP BETWEEN A CAUSE AND ITS EFFECT

Causal reasoning (psychology); User:Lilypad221/sandbox; Causal Reasoning (Psychology); Inductive causal reasoning; Deductive causal reasoning; Abductive causal reasoning

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.

https://en.wikipedia.org/wiki/Causal_reasoning

Causal inference

PROCESS OF DRAWING A CONCLUSION ABOUT A CAUSAL CONNECTION BASED ON THE CONDITIONS OF THE OCCURRENCE OF AN EFFECT

Causal Inference; Algorithms for causal inference; Artificial intelligence and causal inference; Causal machine learning; Causality in machine learning; Causality and machine learning; Machine learning for causal inference

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.

https://en.wikipedia.org/wiki/Causal_inference

Википедия

Causal sets

The causal sets program is an approach to quantum gravity. Its founding principles are that spacetime is fundamentally discrete (a collection of discrete spacetime points, called the elements of the causal set) and that spacetime events are related by a partial order. This partial order has the physical meaning of the causality relations between spacetime events.

The program is based on a theorem by David Malament that states that if there is a bijective map between two past and future distinguishing space times that preserves their causal structure then the map is a conformal isomorphism. The conformal factor that is left undetermined is related to the volume of regions in the spacetime. This volume factor can be recovered by specifying a volume element for each space time point. The volume of a space time region could then be found by counting the number of points in that region.

Causal sets was initiated by Rafael Sorkin who continues to be the main proponent of the program. He has coined the slogan "Order + Number = Geometry" to characterize the above argument. The program provides a theory in which space time is fundamentally discrete while retaining local Lorentz invariance.

https://en.wikipedia.org/wiki/Causal sets