Improved double-robust estimation in missing data and causal inference models.
By Andrea Rotnitzky, Quanhong Lei, Mariela Sued, James M. Robins. 2012
163 citations recorded by the source corpus.
Subfields: Statistics and Probability
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In Statistics and Probability
- Nathan Kallus
- Yu-Xiang Wang
- Karel Vermeulen
- Susan Athey
- Stijn Vansteelandt
- Alexander Luedtke
- Alekh Agarwal
- Stefan Wager
Citations in the corpus
- Optimal and Adaptive Off-policy Evaluation in Contextual Bandits (2016, methodology): “In statistics, this is studied as the problem of mean estimation for treatment regimes in the causal inference literatre (see e.g. [Horvitz and Thompson, 1952, Holland, 1986, Bang and Robins, 2005, Rotnitzky et al., 2012] as well as the references in [Dudík et al., 2014]).”
- Bias-Reduced Doubly Robust Estimation (2015, methodology): “Restrictions on the dimension of the working models are also seen in other proposals (Rotnitzky et al. 2012).”
- Sequential Double Robustness in Right-Censored Longitudinal Models (2017, background): “The current literature appears to define double robustness as follows (van der Laan and Robins, 2003; Bang and Robins, 2005; Tsiatis et al., 2011; Rotnitzky et al., 2012): Definition 1 (Double robustness for Q0).”
- Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, background): “W Iˇ/ j Zº; for more discussions on AIPW estimators and double robustness, please refer to van der Laan & Robins (2003), Bang & Robins (2005), Tsiatis (2006), Qin & Zhang (2007), Cao et al. (2009), Tan (2010), van der Laan and Gruber (2010) and Rotnitzky et al. (2012).”
- Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, methodology): “In their evaluation, Kang and Schafer (2007) used the following simulation study, which has become a standard setting for assessing the performance of weighting methods for causal inference and estimation with incomplete outcome data (e.g., Tan 2010; Rotnitzky et al. 2012; Imai and Ratkovic 2014).”
- Multiply Robust Estimation in Regression Analysis With Missing Data (2014, background): “…have been proposed and studied by Tan (2006, 2008, 2010), Kang and Schafer (2007) and its discussion, Qin and Zhang (2007), Qin, Shao, and Zhang (2008), Rubin and van der Laan (2008), Cao, Tsiatis, and Davidian (2009), Tsiatis, Davidian, and Cao (2011), Han (2012), and Rotnitzky et al. (2012).”