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A Selection Bias Approach to Sensitivity Analysis for Causal Effects

Published online by Cambridge University Press:  04 January 2017

Matthew Blackwell*
Affiliation:
Department of Political Science, University of Rochester, 307 Harkness Hall, Rochester, NY 14627, NY
*
e-mail: m.blackwell@rochester.edu (corresponding author)
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Abstract

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The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Type
Research Article
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Author's note: The methods used in this article are available as an open-source R package, causalsens, on the Comprehensive R Archive Network (CRAN) and the author's web site. The replication archive for this article is available at the Political Analysis Dataverse as Blackwell (2013b). Many thanks to Steve Ansolabehere, Adam Glynn, Gary King, Jamie Robins, Maya Sen, and two anonymous reviewers for helpful comments and discussions. All remaining errors are my own.

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