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Open AccessEssay

Correlations and How to Interpret Them

Collegium Helveticum, ETH Zurich and University of Zurich, 8092 Zurich, Switzerland
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Information 2019, 10(9), 272; https://doi.org/10.3390/info10090272
Received: 7 June 2019 / Revised: 12 August 2019 / Accepted: 21 August 2019 / Published: 29 August 2019
Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical. View Full-Text
Keywords: causation; correlation; data mining; emergence; mind-matter correlation; prediction; quantum correlation; reduction; supervenience causation; correlation; data mining; emergence; mind-matter correlation; prediction; quantum correlation; reduction; supervenience
MDPI and ACS Style

Atmanspacher, H.; Martin, M. Correlations and How to Interpret Them. Information 2019, 10, 272.

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