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A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

  • Tymon Słoczyński (a1) and Jeffrey M. Wooldridge (a2)

Abstract

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

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Corresponding author

*Address correspondence to Jeffrey M. Wooldridge, Department of Economics, Michigan State University, East Lansing, MI 48824-1038, USA; e-mail: wooldri1@msu.edu.

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We thank Peter Phillips (Editor), Arthur Lewbel (Co-Editor), and three anonymous referees for helpful comments. Tymon Słoczyński also acknowledges financial support from the National Science Centre (grant DEC-2012/05/N/HS4/00395) and the Foundation for Polish Science (a START scholarship).

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References

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A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

  • Tymon Słoczyński (a1) and Jeffrey M. Wooldridge (a2)

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