Philosophy and Technology 29 (2):137-171 (2016)

Authors
Wolfgang Pietsch
Technische Universität München
Abstract
I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific knowledge. In particular, it is shown to lack a pronounced hierarchical, nested structure. The significance of the transition to such “horizontal” modeling is underlined by the concurrent emergence of novel inductive methodology in statistics such as non-parametric statistics. Data-intensive modeling is well equipped to deal with various aspects of causal complexity arising especially in the higher level and applied sciences.
Keywords Data-intensive science  Big data  Causation  Eliminative induction  Modeling  Complexity  Explanation
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DOI 10.1007/s13347-015-0202-2
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References found in this work BETA

How the Laws of Physics Lie.Nancy Cartwright - 1983 - Oxford, England: Oxford University Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
The Aim and Structure of Physical Theory.Pierre Maurice Marie Duhem - 1954 - Princeton: Princeton University Press.

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Citations of this work BETA

Algorithmic Accountability and Public Reason.Reuben Binns - 2018 - Philosophy and Technology 31 (4):543-556.
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Simplified Models: A Different Perspective on Models as Mediators.C. D. McCoy & Michela Massimi - 2018 - European Journal for Philosophy of Science 8 (1):99-123.

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