Arbeitspapier
A General Double Robustness Result for Estimating Average Treatment Effects
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 8084
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
inverse-probability weighting (IPW)
multi-valued treatments
quasi-maximum likelihood estimation (QMLE)
treatment effects
Wooldridge, Jeffrey M.
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Sloczynski, Tymon
- Wooldridge, Jeffrey M.
- Institute for the Study of Labor (IZA)
Entstanden
- 2014