Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS
By Stefano Canali. 2016
31 citations recorded by the source corpus.
Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data.
Subfields: Health, Toxicology and Mutagenesis, History and Philosophy of Science, Molecular Biology
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In Health, Toxicology and Mutagenesis
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In Molecular Biology
Citations in the corpus
- A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts (2022, background): “Being equipped with extensive domain knowledge in data-driven science (Canali, 2016) would therefore allow social scientists to transform quantitative analytics into valuable insights for knowledge acquisition.”