Efficiency Bounds for Missing Data Models with Semiparametric Restrictions

By Bryan S. Graham. 2008

66 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ecf51f312cee6abac7dba645644b4b3149bdce40

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In Statistics and Probability

Citations in the corpus

Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, background): “Furthermore, Kennedy, Ma, McHugh, and Small (2017), Semenova and Chernozhukov (2017), Su, Ura, and Zhang (2019), Zimmert and Lechner (2019), and Colangelo and Lee (2020) consider an expectation or quantiles of Y (x1 + ε,X−1) given a value x1 of X1.”
Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions (2017, background): “In the class of general missing data models, eﬃciency bounds have been established under the assumption that the propensity score can be modeled by a ﬁnite-dimensional parameter(Graham (2011), Graham et al. (2012)), which is a restrictive condition.”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, result): “Similar results appear in Birmingham, Rotnitzky, and Fitzmaurice (2003), Robins, Rotnitzky, and Scharfstein (2000), Tan (2006), Graham (2011), Hristache and Patilea (2017), Franks, Amour, and Feller (2020), and Zhao, Small, and Bhattacharya (2019).”
Semi-supervised inference: General theory and estimation of means (2016, background): “Some remarks in the latter paper about MAR data can be used to extend the treatment to ordinary semi-supervision, as can a specialization of the MAR results in Graham (2011).”
Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST) (2016, background): “Graham (2011) studied such restrictions in the missing data context.”