A General Double Robustness Result for Estimating Average Treatment Effects

Tymon Słoczyński and Jeffrey Wooldridge

No 8084, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. 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.

Keywords: double robustness; inverse-probability weighting (IPW); multi-valued treatments; quasi-maximum likelihood estimation (QMLE); treatment effects (search for similar items in EconPapers)
JEL-codes: C13 C21 C31 C51 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2014-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

Published - published in: Econometric Theory, 2018, 34(1), 112–133

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Related works:
Journal Article: A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2018)
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