Undersmoothing and bias corrected functional estimation

By Whitney K. Newey, F. Hsieh, James M. Robins. 1998

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “Newey et al. (1998, 2004) further develop theory in this vein, in a low-dimensional nonparametric setting.”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “h as kernels or series. See, for example, the work by Levit (1975), Ibragimov and Hasminskii (1981), Bickel (1982), Robinson (1988), Newey (1990), van der Vaart (1991), Andrews (1994a), Newey (1994), Newey et al. (1998), Robins and Rotnitzky (1995), Linton (1996), Bickel et al. (1998), Chen et al. (”
Cross-fitting and fast remainder rates for semiparametric estimation (2017, background): “Also, it is known from Newey, Hsieh, and Robins (1998, 2004) and Chernozhukov et. al.”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, background): “(2019) showed that the class strictly includes two classes of parameters studied earlier, one by Robins et al. (2008) and another by Chernozhukov et al.”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, methodology): “The functions e0(·) and p0(·) require modern regularized methods to be consistently estimated, and the regularization bias cannot be made small by standard semiparametric techniques such as undersmoothing (Newey et al. (1998)).”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “…by Levit (1975), Ibragimov and Hasminskii (1981), Bickel (1982), Robinson (1988), Newey (1990), van der Vaart (1991), Andrews (1994a), Newey (1994), Newey et al. (1998), Robins and Rotnitzky (1995), Linton (1996), Bickel et al. (1998), Chen et al. (2003), Newey et al. (2004), van der Laan and Rose…”