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Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response

Published online by Cambridge University Press:  04 January 2017

Gary King  and
Langche Zeng
Gary King*
Affiliation:
Department of Government and Institute for Quantitative Social Science, Harvard University, Cambridge MA 02138
Langche Zeng
Affiliation:
Department of Political Science, University of California, San Diego, La Jolla, CA 92093, e-mail: lazeng@ucsd.edu
*
e-mail: king@harvard.edu (corresponding author)
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Abstract

In response to the data-based measures of model dependence proposed in King and Zeng (2006), Sambanis and Michaelides (2008) propose alternative measures that rely upon assumptions untestable in observational data. If these assumptions are correct, then their measures are appropriate and ours, based solely on the empirical data, may be too conservative. If instead, and as is usually the case, the researcher is not certain of the precise functional form of the data generating process, the distribution from which the data are drawn, and the applicability of these modeling assumptions to new counterfactuals, then the data-based measures proposed in King and Zeng (2006) are much preferred. After all, the point of model dependence checks is to verify empirically, rather than to stipulate by assumption, the effects of modeling assumptions on counterfactual inferences.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 1 , Winter 2009 , pp. 107 - 112
Copyright
Copyright © The Author 2008. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Author's note: Easy-to-use software to implement the methods discussed here, called “WhatIf: Software for Evaluating Counterfactuals,” is available at http://gking.harvard.edu/whatif. All information necessary to replicate the analyses herein can be found in King and Zeng (2008). Conflict of interest statement. None declared.

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