@TechReport{iza:izadps:dp8084,
            	author={Sloczynski, Tymon and Wooldridge, Jeffrey M.},
            	title={A General Double Robustness Result for Estimating Average Treatment Effects},
            	year={2014},
	month={Mar},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={8084},
            	url={https://www.iza.org/index.php/publications/dp8084},
	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},
            }