 | Look up causality or causal in Wiktionary, the free dictionary. |
Causality is the influence that connects one process or state, the cause, with another process or state, the effect, where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.
Causality may also refer to:
The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc. This differs from the fallacy known as post hoc ergo propter hoc, in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one.
An epiphenomenon is a secondary phenomenon that occurs alongside or in parallel to a primary phenomenon. The word has two senses: one that connotes known causation and one that connotes absence of causation or reservation of judgment about it.
Causality is the relationship between causes and effects. While causality is also a topic studied from the perspectives of philosophy, from the perspective of 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. Similarly, a cause cannot have an effect outside its future light cone.
Libertarianism is one of the main philosophical positions related to the problems of free will and determinism which are part of the larger domain of metaphysics. In particular, libertarianism is an incompatibilist position which argues that free will is logically incompatible with a deterministic universe. Libertarianism states that since agents have free will, determinism must be false.
The Markov condition, sometimes called the Markov assumption, is an assumption made in Bayesian probability theory, that every node in a Bayesian network is conditionally independent of its nondescendents, given its parents. Stated loosely, it is assumed that a node has no bearing on nodes which do not descend from it. In a DAG, this local Markov condition is equivalent to the global Markov condition, which states that d-separations in the graph also correspond to conditional independence relations. This also means that a node is conditionally independent of the entire network, given its Markov blanket.
Wesley Charles Salmon was an American philosopher of science renowned for his work on the nature of scientific explanation. He also worked on confirmation theory, trying to explicate how probability theory via inductive logic might help confirm and choose hypotheses. Yet most prominently, Salmon was a realist about causality in scientific explanation, although his realist explanation of causality drew ample criticism. Still, his books on scientific explanation itself were landmarks of the 20th century's philosophy of science, and solidified recognition of causality's important roles in scientific explanation, whereas causality itself has evaded satisfactory elucidation by anyone.
Physical causal closure is a metaphysical theory about the nature of causation in the physical realm with significant ramifications in the study of metaphysics and the mind. In a strongly stated version, physical causal closure says that "all physical states have pure physical causes" — Jaegwon Kim, or that "physical effects have only physical causes" — Agustin Vincente, p. 150.
The problem of mental causation is a conceptual issue in the philosophy of mind. That problem, in short, is how to account for the common-sense idea that intentional thoughts or intentional mental states are causes of intentional actions. The problem divides into several distinct sub-problems, including the problem of causal exclusion, the problem of anomalism, and the problem of externalism. However, the sub-problem which has attracted most attention in the philosophical literature is arguably the exclusion problem.
In philosophy of science, a causal model is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
Accident analysis is carried out in order to determine the cause or causes of an accident so as to prevent further accidents of a similar kind. It is part of accident investigation or incident investigation. These analyses may be performed by a range of experts, including forensic scientists, forensic engineers or health and safety advisers. Accident investigators, particularly those in the aircraft industry, are colloquially known as "tin-kickers". Health and safety and patient safety professionals prefer using the term "incident" in place of the term "accident". Its retrospective nature means that accident analysis is primarily an exercise of directed explanation; conducted using the theories or methods the analyst has to hand, which directs the way in which the events, aspects, or features of accident phenomena are highlighted and explained.
Probabilistic causation is a concept in a group of philosophical theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes raise the probabilities of their effects, all else being equal.
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. The first known protoscientific study of cause and effect occurred in Aristotle's Physics. Causal inference is an example of causal reasoning.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.
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. They were established in 1965 by the English epidemiologist Sir Austin Bradford Hill.
In philosophy, a grouped event is the experience of two or more events that occur in sequence or concurrently that can be subsequently categorized.
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. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis
Universal causation is the proposition that everything in the universe has a cause and is thus an effect of that cause. This means that if a given event occurs, then this is the result of a previous, related event. If an object is in a certain state, then it is in that state as a result of another object interacting with it previously.
The Book of Why: The New Science of Cause and Effect is a 2019 nonfiction book by Judea Pearl and Dana Mackenzie. Pearl is a computer scientist and philosopher and Mackenzie is a science writer. The book explores the subject of causality and causal inference from statistical and philosophical points of view. The book is aimed at a general audience.