Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

The Causal Nature of Modeling with Big Data

The Causal Nature of Modeling with Big Data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Philosophy & Technology Springer Journals

The Causal Nature of Modeling with Big Data

Philosophy & Technology , Volume 29 (2) – Jun 19, 2015

Loading next page...
 
/lp/springer-journals/the-causal-nature-of-modeling-with-big-data-LPL9ORAJES
Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer Science+Business Media Dordrecht
Subject
Philosophy; Philosophy of Technology
ISSN
2210-5433
eISSN
2210-5441
DOI
10.1007/s13347-015-0202-2
Publisher site
See Article on Publisher Site

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.

Journal

Philosophy & TechnologySpringer Journals

Published: Jun 19, 2015

References