Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators

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Causal mediation analysis for stochastic interventions (2019, unknown): “(Carpenter et al., 2006; Vansteelandt et al., 2010; Seaman et al., 2012).”
Combining Multiple Imputation and Inverse-Probability Weighting (2012, background): “However, the fit of the missingness model, which is a model for a univariate response, is easier to assess, and more able to be assessed (Vansteelandt, Carpenter, and Kenward, 2010), than that of a complex multivariate imputation model.”
On model selection and model misspecification in causal inference (2012, unknown): “Robins and Ritov(47) underscored more formally the importance of using information on the exposure distribution in causal inference by demonstrating that, due to the curse of dimensionality, likelihood-based procedures fail to estimate treatment effects in randomised experiments where randomisation ”
Review of inverse probability weighting for dealing with missing data (2013, methodology): “AIPW is more complicated to apply in standard software and we describe it here only briefly; Vansteelandt et al.4 provide an accessible introduction.”