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Assessing Threats to Inference with Simultaneous Sensitivity Analysis: The Case of US Supreme Court Oral Arguments*

Published online by Cambridge University Press:  10 December 2015

Jeffrey Budziak  and
Daniel Lempert
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Abstract

Political scientists relying on observational data face substantial challenges in drawing causal inferences. A particularly problematic threat to inference is the unobserved confounder. As a means to assess this threat, we introduce simultaneous sensitivity analysis to the political science literature. As an application, we consider the potentially confounded relationship between Supreme Court justice voting and oral argument quality. We demonstrate that this relationship is sensitive to the presence of a confounder, to a degree that threatens inference, and explore the confounder both theoretically and empirically. More generally, we show how sensitivity analysis can guide inquiry related to a covariate that cannot be directly measured.

Type
Original Articles
Information
Political Science Research and Methods , Volume 6 , Supplement 1 , January 2018 , pp. 33 - 56
Copyright
© The European Political Science Association 2015 

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Footnotes

*

Jeffrey Budziak is an Assistant Professor in the Department of Political Science, Western Kentucky University, 1906 College Heights Blvd., 300 Grise Hall, Bowling Green, KY 42101 ([email protected]). Daniel Lempert is an Assistant Professor in the Department of Politics, SUNY Potsdam, 44 Pierrepont Avenue, Potsdam, NY 13676 ([email protected]). A version of this paper was presented at the 2010 Conference on Empirical Legal Studies at Yale University. For helpful comments, discussion, and suggestions, the authors thank Larry Baum, Greg Caldeira, William Minozzi, and Dylan Small. The authors thank Timothy Johnson, James Spriggs, and Paul Wahlbeck for making their data available. To view supplementary material for this article, please visit http://dx.doi.org/10.1017/psrm.2015.74

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