Analysis of multivariate missing data with nonignorable nonresponse

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A New Instrumental Method for Dealing with Endogenous Selection (2010, methodology): “This assumption has been also used in the framework of nonignorable nonresponse by [6], [21], [42] and [43].”
Local model uncertainty and incomplete‐data bias (with discussion) (2005, background): “Identifiability may come through strong assumptions on other aspects of the model; for example Tang et al. (2003) assumed that the marginal distribution of covariates is known.”
Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data (2015, methodology): “Under their pseudolikelihood approach, Tang, Little, and Raghunathan (2003) studied the identifiability issue for responses subject to missingness with the additional assumption that, conditional on the responses, all the covariates are independent with the missing data mechanism.”
An Instrumental Variable Approach for Identification and Estimation with Nonignorable Nonresponse (2014, background): “It has been studied in the case of parametric p(y|x) (Chen (2001)); Tang, Little, and Raghunathan (2003)) and some semiparametric p(y|x) (Rotnitzky and Robins (1997)), but it is not well studied in the case of nonparametric p(y|x).”
A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data (2011, background): “Chen (2001) and Tang, Little, and Raghunathan (2003) discussed identifiability conditions only under some limited situations.”