Journal of Economic Methodology 13 (2):219-240 (2006)

James Woodward
University of Pittsburgh
It is widely believed that robustness (of inferences, measurements, models, phenomena and relationships discovered in empirical investigation etc.) is a Good Thing. However, there are many different notions of robustness. These often differ both in their normative credentials and in the conditions that warrant their deployment. Failure to distinguish among these notions can result in the uncritical transfer of considerations which support one notion to contexts in which another notion is being deployed. This paper surveys several different notions of robustness and tries to identify why (and in what circumstances) each is valuable or appealing. I begin by discussing the notion of robustness addressed in Aldrich's paper (robustness as insensitivity of the results of inference to alternative specifications) and then discuss how this relates to robustness of derivations, robustness of measurement results, and robustness as a mark of casual as opposed to (merely) correlational relationships.
Keywords Causation   Economics
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DOI 10.1080/13501780600733376
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References found in this work BETA

The Structure of Scientific Revolutions.Thomas Samuel Kuhn - 1962 - Chicago: University of Chicago Press.
The Logic of Scientific Discovery.Karl Raimund Popper - 1934 - London, England: Routledge.
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Citations of this work BETA

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The Ontic Account of Scientific Explanation.Carl F. Craver - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the Special Sciences: The Case of Biology and History. Springer Verlag. pp. 27-52.
Robustness Analysis as Explanatory Reasoning.Jonah N. Schupbach - 2016 - British Journal for the Philosophy of Science:axw008.
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