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Partial Identification and Sensitivity Analysis

Part of the Handbooks of Sociology and Social Research book series (HSSR)

Abstract

This chapter is concerned with methods of causal inference in the presence of unobserved confounders. Three classes of estimators are discussed, namely, local identification using instrumental variables, sensitivity analysis, and estimation of nonparametric bounds. In each case, the response to the core identification problem is to retreat from the standard focus on point identification of the average treatment effect, yet the three approaches characteristically differ in terms of alternative quantities of interest that are considered empirically estimable under more restrictive circumstances. The chapter develops the basic principles underlying the three classes of partial identification estimators and illustrates their empirical application with an analysis of earnings returns to education.

Keywords

  • Ordinary Little Square
  • Instrumental Variable
  • Average Treatment Effect
  • Fixed Effect
  • Ordinary Little Square Estimate

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

The GSOEP data have kindly been provided by the Deutsche Institut für Wirtschaftsforschung (DIW), Berlin. Of course, the DIW does not bear any responsibility for the uses made of the data, nor the inferences drawn by the author. I thank Stephen Morgan, Jan Brülle, and Fabian Ochsenfeld for helpful comments on an earlier draft of this chapter.

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Correspondence to Markus Gangl .

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Gangl, M. (2013). Partial Identification and Sensitivity Analysis. In: Morgan, S. (eds) Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6094-3_18

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