Review of inverse probability weighting for dealing with missing data

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Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “Propensity-based approaches have been widely used in causal inference from observational studies (Imbens & Ru-bin, 2015), as well as in complete-case analysis for missing data (Little & Rubin, 2002; Seaman & White, 2013) and in survey sampling (Thompson, 2012).”
Accounting for missing data in statistical analyses: multiple imputation is not always the answer (2019, methodology): “In this situation, MI results may also be highly susceptible to misspecification of the imputation model.(23) Therefore, in this situation, CCA may be preferred to MI.”
Learning under selective labels in the presence of expert consistency (2018, neutral): “Statistics and quantitative methods literature on missing data has also addressed this problem (Little & Rubin, 2014; Seaman & White, 2013).”
The proportion of missing data should not be used to guide decisions on multiple imputation (2019, methodology): “If we cannot correctly specify the imputation model, then alternatives to MI such as inverse probability weighting [44] or study-specific sensitivity analysis may be preferable.”
Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness (2014, methodology): “These data were also analyzed by Thomas et al. (2007) and Seaman and White (2013).”