Affording Visual Causal Epistemologies in Epidemiology

Jordi Vallverdú*

Received: November 14, 2017;   Published: December 01, 2017

DOI: 10.26717/BJSTR.2017.01.000557

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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 acyclic graphs (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.

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