Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency

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Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “On the other hand, weaker variance-related assumptions, or “overfitting” robust inference procedures, (Cattaneo and Jansson, 2018; Cattaneo et al., 2018), may also be possible following deep learning, but are less automatic at present.”
Locally Robust Semiparametric Estimation (2016, methodology): “It may be possible to weaken this condition by bias correcting (  ̂) as by the bootstrap in Cattaneo and Jansson (2017), by the jackknife in Cattaneo Ma and Jansson (2017), and by cross-fitting in Newey and Robins (2017).”
Cross-fitting and fast remainder rates for semiparametric estimation (2017, background): “Cattaneo and Jansson (2017) propose promising bootstrap confidence intervals for plug-in kernel estimators that include bias corrections. Also, Cattaneo, Jansson, and Ma (2017) show that the jackknife can be used to reduce bias of plug-in series estimators.”
Robust Inference Using Inverse Probability Weighting (2018, methodology): “With the IPW estimator as a special case, Cattaneo and Jansson (2018) and Cattaneo, Jansson and Ma (2019) show how an asymptotic bias can arise in a two-step semiparametric setting as a result of overfitting the first step.”