Abstract
One of the challenges of 21st Century sciences is how to deal
with and manage huge amounts of raw data [1] Using several
computational tools, scientists are able to capture, process
and, finally, to understand that data. The visual aspects of this
understanding process are of the utmost importance due to the
specific cognitive mechanisms that make possible human thinking
[2]. Epidemiology is a very complex research field devoted to
the study of health and the causes of illness [3]. The difficulty of
establishing sound statistical relationships between sets of events
and some causal outcomes [4] has been the main source of debates
within the field [5-7] Although epidemiologists and physicians have
tried to avoid philosophical debates [8] about causality, it has been
impossible to not be aware of the intrinsic and insurmountable
problem of working with so complex amounts of data. From the
simple one-hit paradigm of early epidemiology [9,10] to current
multi-causal webs of determinants [11], new challenges have
emerged. A possible solution for the management of such sets of
data has been to invest into visual causal methods: directed acyclicgraphs (henceforth, DAG). These methods have allowed a visual
quantitative approach to epidemiology [12-14] that fits perfectly
with the current research trends in cognitive sciences which defend
the power of extended and enhanced ways of using informational
tools, which afford new and sound ways of processing information.
Affording Visual Causal #Epistemologies in Epidemiology by Jordi Vallverdú in BJSTR
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