David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 180 (1):65 - 76 (2011)
Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science
|Keywords||Models Data Inductive inference Nonparametric statistics Bootstrap resampling|
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Clark Glymour (1980). Theory and Evidence. Princeton University Press.
Peter Godfrey-Smith (2006). The Strategy of Model-Based Science. Biology and Philosophy 21 (5):725-740.
Peter Spirtes, Clark Glymour & Richard Scheines (1996). Causation, Prediction, and Search. British Journal for the Philosophy of Science 47 (1):113-123.
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Citations of this work BETA
Wolfgang Pietsch (2016). The Causal Nature of Modeling with Big Data. Philosophy and Technology 29 (2):137-171.
Kent Staley (2012). Strategies for Securing Evidence Through Model Criticism. European Journal for Philosophy of Science 2 (1):21-43.
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