Disponible online en: pitt.edu

It has been argued that the use of Big Data in scientific research casts doubt on the need for causal knowledge in making sound predictions (Mayer-Schonberger & Cukier, 2013). In this article I argue that it is possible to search for productive causes in Big Data if one adopts the 'information-transfer account of causation' (Illari, 2011; Illari & Russo, 2014), a version of the causal process theory. As it stands, the current formulation is inadequate as it does not specify how information is to be measured. I consider three concepts of information: (i) information as knowledge update, (ii) information as entropy and (iii) information as algorithmic complexity, and argue that the last of these provides the best way to achieve this with respect to Big Data. How this can be used to search for causal connections among Big Data is then illustrated with respect to exposomics research.