Semiparametric Efficiency in Multivariate Regression Models with Missing Data

By James M. Robins, Andrea Rotnitzky. 1995

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, background): “The topic of optimal policy estimation has received some attention in the ML literature, focusing on data from observational studies with unconfoundedness, including Strehl et al. [2010], Dudik et al.”
Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “Doubly robust estimating equations as in Robins et al. (1995) and Robins and Rotnitzky (1995) have this structure.”
Double/Debiased/Neyman Machine Learning of Treatment Effects (2017, methodology): “Neyman-orthogonal scores are readily available for both the ATE and ATTE – one can employ the doubly robust/efficient scores of Robins and Rotnitzky (1995) and Hahn (1998), which are automatically Neyman orthogonal.”
The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “Regression Discontinuity Designs have a long history, going back to work in psychology in the fifties by Thistlewaite and Campbell [1960], but the methods did not become part of the mainstream economics literature until the early 2000s (with Goldberger [1972, 2008] an exception).”
Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, background): “·/ or the outcome fit β̂c is consistent, and they are asymptotically efficient in a semiparametric specification (Hahn, 1998; Hirano et al., 2003; Robins and Rotnitzky, 1995; Tsiatis, 2007).”
Doubly Robust Policy Evaluation and Optimization (2014, background): “Doubly robust (or doubly protected) estimation (Cassel, Särndal and Wretman, 1976; Robins, Rotnitzky and Zhao, 1994; Robins and Rotnitzky, 1995; Lunceford and Davidian, 2004; Kang and Schafer, 2007) is a statistical approach for estimation from incomplete data with an important property: if either o”
Learning When-to-Treat Policies (2019, methodology): “We propose an advantage doubly robust estimator that draws upon the semiparametrics and orthogonal moments literature (Belloni et al., 2014; Chernozhukov et al., 2016a; Newey, 1994; Robins and Rotnitzky, 1995; Scharfstein et al., 1999).”
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits (2016, methodology): “, [11, 1]), combine the model with an IPS-style 42 unbiased estimation and remain consistent, with sharp estimates of the MSE.”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, background): “they remain asymptotically unbiased if one of the two models is misspecified—that is, they consistently estimate their targets if either model is true (Robins and Rotnitzky, 1995).”
Efficient Policy Learning (2017, methodology): “…bounds available for different policy learners, and concretely establishes the relevance of the classic literature on semiparametrically efficient estimation (Bickel et al., 1998; Hahn, 1998; Hirano et al., 2003; Newey, 1994; Robins and Rotnitzky, 1995; Robins et al., 1995) to policy learning.”
Quasi-oracle estimation of heterogeneous treatment effects (2017, methodology): “Our approach is motivated by the transformation of Robinson (1988), and draws more broadly from the literature on semiparametric inference and constructions of orthogonal moments (Athey and Wager, 2017; Belloni et al., 2017; Bickel et al., 1998; Luedtke and van der Laan, 2016; Newey, 1994; Robins, 2”
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning (2019, methodology): “The most prominent one is the Doubly Robust (DR) estimator (Robins & Rotnitzky, 1995; Kang et al., 2007; Dud´ık et al., 2011), which is based on DM but also uses IPS weighting and an additive control variate to reduce variance.”
Bayesian Nonparametric Modeling for Causal Inference (2011, methodology): “Moreover, semiparametric estimators of this sort, such as those discussed by Robins and Rotnitzky (1995) and Rotnitzky, Robins, and Scharfstein (1998), were shown by Scharfstein, Rotnitzky, and Robins (1999) to be consistent as long as either the treatment assignment mechanism or the response…”
Dealing with limited overlap in estimation of average treatment effects (2009, unknown): “see Rosenbaum (2001) and Imbens (2004) for general surveys.”
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score (2000, unknown): “The rst case is the missing data case studied by Robins and Rotnitzky 1995 and Rotnitzky and Robins 1995, with the missing data assumed to be missing at random.”