MINIMAX ESTIMATION OF A FUNCTIONAL ON A STRUCTURED

By James, Robins, Lingling Li, Rajarshi Mukherjee, E. T. Tchetgen Tchetgen, A. van der Vaart. 2016

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The hardness of conditional independence testing and the generalised covariance measure (2018, methodology): “Estimators of the expected conditional covariance functional requiring minimal possible smoothness conditions may be derived using the theory of higher order influence functions [42, 43, 34, 44]; these estimators are however significantly more complicated.”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “…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, 2004; Robins and Rotnitzky, 1995; Robins et al., 2017; Scharfstein et al., 1999; Tsiatis, 2007; van der Laan and Rose, 2011).”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, background): “As a result, conditions (27) and (28) are satisfied by π̄1 CAL(X) = π(X; γ̄ 1 CAL) and m̄(1)WL(X) = m 1(X; ᾱ1 WL), where γ̄ 1 CAL and ᾱ 1 WL are, respectively, the probability limits of γ̂1 RCAL and α̂ 1 RWL, defined as minimizers of the expected loss E{`CAL(γ)} and E{`WL(α(1); γ̄1 CAL)}.”
Cross-fitting and fast remainder rates for semiparametric estimation (2017, background): “Robins et al.(2008, 2017) and Mukherjee, Newey, and Robins (2017) develop higher order inﬂu-ence function (HOIF) estimators with smaller bias.”
Augmented minimax linear estimation (2017, background): “As discussed above, our approach is primarily motivated as a refinement of minimax linear estimators as developed and studied by a large community over the past decades (Armstrong and Kolesár, 2018; Cai and Low, 2003; Donoho, 1994; Donoho and Liu, 1991; Ibragimov and Khas’minskii, 1985; Imbens and W”
Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, methodology): “They also thank Songnian Chen, Edward Kennedy, Whitney Newey, Ryo Okui, Yoon-Jae Whang, and participants of California Econometrics Conference (Irvine), Cemmap-WISE Joint Workshop, AMES 2019, for their valuable comments.”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, background): “In fact, Rotnitzky et al. (2019) showed that the class strictly includes two classes of parameters studied earlier, one by Robins et al.”
Semiparametric Counterfactual Density Estimation (2021, unknown): “Estimation and inference for parameters like (9) is relatively well-understood [Robins et al., 2009, 2017], which allows exploiting existing theory and methods in the density estimation context.”
Policy Learning With Observational Data (2017, methodology): “The K -fold algorithmic structure used in (18) was proposed in an early paper by Schick (1986) as a general purpose tool for eﬃcient estimation in semiparametric models, and has also been used by other authors including Robins et al. (2017) and Zheng and van der Laan (2011).”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “Robins et al. (2008) and Robins et al. (2017) use sample splitting in the construction of higher-order influence function corrections in semiparametric estimation.”