A General Double Robustness Result for Estimating Average Treatment Effects

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 8084

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Model Construction and Estimation
• Subject: double robustness ; inverse-probability weighting (IPW) ; multi-valued treatments ; quasi-maximum likelihood estimation (QMLE) ; treatment effects

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Sloczynski, Tymon ; Wooldridge, Jeffrey M.
• Event: Veröffentlichung
• (who): Institute for the Study of Labor (IZA)
• (where): Bonn
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/96680
• Last update: 20.09.2024, 8:22 AM CEST