Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: Rejoinder

By D. Scharfstein, Andrea Rotnitzky, James M. Robins. 1999

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Citations in the corpus

Double/debiased machine learning for treatment and structural parameters (2017, unknown): “There is also a related targeted maximum likelihood learning approach, introduced in Scharfstein et al. (1999) in the context of treatments effects analysis and substantially generalized by van der Laan and Rubin (2006). van der Laan and Rubin (2006) use maximum likelihood in a least favorable…”
Inverse probability weighted estimation for general missing data problems (2004, methodology): “For the treatment effect case, the setup allows a general characterization of a “double robustness” result due to Scharfstein, Rotnitzky, and Robins (1999).”
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, background): “The authors focus on the performance of doubly robust estimators (Robins et al. 1994; Scharfstein et al. 1999) when neither the probability model nor the outcome model are severely misspecified.”
An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals (2004, background): “Scharfstein, Rotnitzky, and Robins (1999a, 1999b) further expanded this approach by allowing the dropout model to be semiparametric.”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, unknown): “That this estimator is robust to misspecification of at most one of the working models was demonstrated in later work by Scharfstein, Rotnitzky, and Robins (1999), and the term “doubly robust” was introduced by Robins, Rotnitzky, and van der Laan (2000).”
An Introduction to the Augmented Inverse Propensity Weighted Estimator (2010, background): “For Permissions, please email: journals.permissions@oxfordjournals.org 36 consistent for the ATE whenever (1) the propensity score model is correctly specified or (2) the outcome regression is correctly specified (Scharfstein, Rotnitzky, and Robins 1999).”
Bias-Reduced Doubly Robust Estimation (2015, methodology): “To construct a DR estimator for the target parameter μ0 (Scharfstein, Rotnitzky, and Robins 1999b), we need two working models: (i) a model M (β) for the unknown function m0(X) given by m(X; β), where m(X; β) is a known function smooth in a finite-dimensional parameter β and (ii) a model M (γ ) for…”