Bounded, efficient and doubly robust estimation with inverse weighting

By Zhiqiang Tan. 2010

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In Statistics and Probability

Citations in the corpus

Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “Motivated by this setup, there is a large literature on how to adjust for differences in observed features between the treatment and control groups; some popular methods include regression, matching, propensity score weighting and subclassification, as well as doubly-robust combinations thereof (e.g”
Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis (2012, methodology): “This result is then combined with the efficient influence function for the functional δe [Robins, Rotnitzky and Zhao (1994), Hahn (1998)] to obtain the efficient influence function for the natural direct and indirect effects on the mean difference scale.”
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).”
Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements (2018, methodology): “…researchers have proposed methods that estimate propensity scores by optimizing covariate balance [e.g., McCaffrey, Ridgeway and Morral (2004), Tan (2010), Hainmueller (2012), Graham, Pinto and Egel (2012), Imai and Ratkovic (2014), Chan, Yam and Zhang (2016), Zhu, Coffman and Ghosh (2015),…”
Inverse Probability Tilting for Moment Condition Models with Missing Data (2012, positive): “A leading one, which shares IPT’s local efficiency and double robustness properties, is the augmented inverse probability weighting (AIPW) estimator introduced byRobins, Rotnitzky and Zhao (1994).2 We characterize theN−1 order asymptotic bias of IPT and a class of AIPW estimators under conditions wh”
Multiply Robust Estimation in Regression Analysis With Missing Data (2014, methodology): “Refer to, for example, Tan (2006, 2008, 2010), Cao, Tsiatis, and Davidian (2009), Tsiatis, Davidian, and Cao (2011), Han (2012), and Rotnitzky et al. (2012).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, background): “In particular, Cao, Tsiatis, and Davidian (2009) and Tan (2010) provided new estimators with increased efficiency and improved robustness against very small values of the propensity score.”
Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data (2017, methodology): “…and sometimes independently (re)derived, in various contexts of causal inference, missing-data problems and survey sampling (e.g., Folsom, 1991; Tan, 2010; Graham et al., 2012; Hainmueller, 2012; Imai & Ratkovic, 2014; Kim & Haziza, 2014; Vermeulen & Vansteelandt, 2015; Chan et al., 2016; Yiu…”
Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting (2015, positive): “Many recent methods focus on improving covariate balance within the propensity score and outcome regression frameworks (Qin and Zhang, 2007; Tan, 2010; Chan, 2012; Graham et al., 2012; Vansteelandt et al., 2012; Han and Wang, 2013; Chan and Yam, 2014).”
Estimation of causal effects with multiple treatments: a review and new ideas (2017, background): “These include a doubly robust approach (Tan, 2010), a covariate balancing propensity score (Imai and Ratkovic, 2014), and generalized boosted models (McCaffrey, Ridgeway and Morral, 2004; McCaffrey et al.”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, positive): “where p0({xi ′γ∗ t }NT ) = 1 − ∑ t∈NT p̂t({x ∗ i ′γ∗ t }NT ) and the first inequality is also due to Tanabe and Sagae (1992). These results imply that v′ iH({xi γt}NT )vi ≥ (pmin/Ap)v iITvi = (pmin/Ap)‖vi‖(2)2 and therefore”
Bias-Reduced Doubly Robust Estimation (2015, methodology): “In that sense, it differs from the various other targeted proposals that have been made over recent years (Cao, Tsiatis, and Davidian 2009; Tan 2010; van der Laan and Gruber 2010; Tsiatis, Davidian, and Cao 2011; van der Laan and Rose 2011; Rotnitzky et al. 2012; van der Laan 2014), some of which…”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, background): “(7) See Kang and Schafer (2007) and Tan (2010) for reviews in low-dimensional settings.”