Estimation with missing data: beyond double robustness

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Empirical Likelihood Estimation Using Auxiliary Summary Information with Different Covariate Distributions (2019, background): “However, this way is not recommended owning to its unstable behavior: equations (3.3) and (3.7)– (3.8), viewed as equations for λ and (λT, ρT)T, respectively, for a fixed β, typically have many roots (Han and Wang (2013)).”
Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, methodology): “Han & Wang (2013) and Chan & Yam (2014) considered estimating the population mean, where the former allows multiple models for both the selection probability and the data distribution, and the latter allows multiple models only for the data distribution.”
Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting (2015, unknown): “Many recent methods focus on improving covariate balance within the propensity score and outcome regression frameworks (Qin and Zhang, 2007; Tan, 2010; Chan, 2012; Graham et al., 2012; Vansteelandt et al., 2012; Han and Wang, 2013; Chan and Yam, 2014).”