A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

By Tymon Słoczyński, Jeffrey M. Wooldridge. 2014

101 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “Only trivial extensions to the above would be required for other causal effects, such as multi-valued treatments (reviewed by Cattaneo, 2010) and others with doubly-robust estimators (Sloczynski and Wooldridge, 2018).”
Econometric Methods for Program Evaluation (2018, background): “Among them are the doubly robust estimators (or, more generally, locally robust estimators) in Van der Laan and Robins (2003); Bang and Robins (2005); Cattaneo (2010); Farrell (2015); Chernozhukov et al. (2016); Sloczynski and Wooldridge (2017).”