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Causality is an influence by which one event, process, state, or object (acause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space.
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.
Determinism is the philosophical view that all events in the universe, including human decisions and actions, are causally inevitable. Deterministic theories throughout the history of philosophy have developed from diverse and sometimes overlapping motives and considerations. Like eternalism, determinism focuses on particular events rather than the future as a concept. The opposite of determinism is indeterminism, or the view that events are not deterministically caused but rather occur due to chance. Determinism is often contrasted with free will, although some philosophers claim that the two are compatible.
Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
The science of epidemiology has matured significantly from the times of Hippocrates, Semmelweis and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.
Malignancy is the tendency of a medical condition to become progressively worse; the term is most familiar as a characterization of cancer.
A case–control study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have the condition with patients who do not have the condition but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio. Some statistical methods make it possible to use a case–control study to also estimate relative risk, risk differences, and other quantities.
In epidemiology, a risk factor or determinant is a variable associated with an increased risk of disease or infection.
The deductive-nomological model of scientific explanation, also known as Hempel's model, the Hempel–Oppenheim model, the Popper–Hempel model, or the covering law model, is a formal view of scientifically answering questions asking, "Why...?". The DN model poses scientific explanation as a deductive structure, one where truth of its premises entails truth of its conclusion, hinged on accurate prediction or postdiction of the phenomenon to be explained.
Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. A norm of reaction is a graph that shows the relationship between genes and environmental factors when phenotypic differences are continuous. They can help illustrate GxE interactions. When the norm of reaction is not parallel, as shown in the figure below, there is a gene by environment interaction. This indicates that each genotype responds to environmental variation in a different way. Environmental variation can be physical, chemical, biological, behavior patterns or life events.
In metaphysics, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
Harold Kelley's covariation model is an attribution theory in which people make causal inferences to explain why other people and ourselves behave in a certain way. It is concerned with both social perception and self-perception.
Metastatic breast cancer, also referred to as metastases, advanced breast cancer, secondary tumors, secondaries or stage IV breast cancer, is a stage of breast cancer where the breast cancer cells have spread to distant sites beyond the axillary lymph nodes. There is no cure for metastatic breast cancer; there is no stage after IV.
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.
A network is an abstract structure capturing only the basics of connection patterns and little else. Because it is a generalized pattern, tools developed for analyzing, modeling and understanding networks can theoretically be implemented across disciplines. As long as a system can be represented by a network, there is an extensive set of tools – mathematical, computational, and statistical – that are well-developed and if understood can be applied to the analysis of the system of interest.
Cause, also known as etiology and aetiology, is the reason or origination of something.
A lung cavity or pulmonary cavity is an abnormal, thick-walled, air-filled space within the lung. Cavities in the lung can be caused by infections, cancer, autoimmune conditions, trauma, congenital defects, or pulmonary embolism. The most common cause of a single lung cavity is lung cancer. Bacterial, mycobacterial, and fungal infections are common causes of lung cavities. Globally, tuberculosis is likely the most common infectious cause of lung cavities. Less commonly, parasitic infections can cause cavities. Viral infections almost never cause cavities. The terms cavity and cyst are frequently used interchangeably; however, a cavity is thick walled, while a cyst is thin walled. The distinction is important because cystic lesions are unlikely to be cancer, while cavitary lesions are often caused by cancer.
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 set of proposed causal mechanisms are represented as pie charts where each pie in the diagram represent a theoretical causal mechanism for a given disease, which is also called a sufficient cause. Each pie is made up of many component factors, otherwise known as component causes represented by sectors in the diagram. In this framework, each component cause represents an event or condition required for a given disease or outcome. A component cause that appears in every pie is called a necessary cause as the outcome cannot occur without it.
Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. For example, the admission of a student into a Ph.D. program necessitates a prior degree; the progression of AIDS necessitates the presence of HIV; and organizational change necessitates communication.
References
- ↑ Rothman KJ (1976). "Causes". Am J Epidemiol. 104 (6): 587–92. doi:10.1093/oxfordjournals.aje.a112335. PMID 998606.
- ↑ Rothman, Kenneth J. (2012). Epidemiology : an introduction (2nd ed.). New York, NY: Oxford University Press. p. 24. ISBN 978-0-19-975455-7. OCLC 750986180.
