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The Fate of Explanatory Reasoning in the Age of Big Data

Cabrera, Frank (2020) The Fate of Explanatory Reasoning in the Age of Big Data. Philosophy and Technology.

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Abstract

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a prominent account of scientific method, often called “Inference to the Best Explanation” (IBE). Later on, in section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely the argument due to Roche and Sober (2013) that “explanatoriness is evidentially irrelevant”. This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In section 4, I consider some extant responses to this argument, especially that of Climenhaga (2017). In section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, (CCR), which states roughly that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations”, chance correlations which we should not rely upon for predictive inference. In section 7, I offer some concluding remarks.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Cabrera, Frank
Keywords: Inference to the Best Explanation Bayesianism Scientific Inference Cicero Big Data Data-Intensive Science Data-Driven Science
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Computation/Information
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Explanation
General Issues > Technology
Depositing User: Frank Cabrera
Date Deposited: 04 Aug 2020 03:25
Last Modified: 04 Aug 2020 03:25
Item ID: 17693
Journal or Publication Title: Philosophy and Technology
Subjects: General Issues > Data
General Issues > Causation
Specific Sciences > Computation/Information
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Explanation
General Issues > Technology
Date: 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/17693

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