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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 analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...
Since causality is a subtle metaphysical notion, considerable intellectual effort, along with exhibition of evidence, is needed to establish knowledge of it in particular empirical circumstances. According to David Hume, the human mind is unable to perceive causal relations directly.
Hill would later outline "Bradford Hill criteria" for establishing causal relationships between statistically correlated phenomena. [7] This publication laid the groundwork for CIOMS' 1982, 1993, 2002, 2009, and 2016 versions of International Ethical Guidelines for Health-Related Research Involving Humans. [3]
Koch's postulates (/ k ɒ x / KOKH) [2] are four criteria designed to establish a causal relationship between a microbe and a disease. The postulates were formulated by Robert Koch and Friedrich Loeffler in 1884, based on earlier concepts described by Jakob Henle, and the statements were refined and published by Koch in 1890. [3]
Causal research, is the investigation of (research into) cause-relationships. [ 1 ] [ 2 ] [ 3 ] To determine causality, variation in the variable presumed to influence the difference in another variable(s) must be detected, and then the variations from the other variable(s) must be calculated (s).
In 1996, Fredricks and Relman proposed seven molecular guidelines for establishing microbial disease causation: [3] "A nucleic acid sequence belonging to a putative pathogen should be present in most cases of an infectious disease.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.