A General Imputation Methodology for Nonparametric Regression with Censored Data

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Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “We use data from the 1994-1996 Canadian National Private Vehicle Use Survey (Semenova and Chernozhukov (2021b)), using sample selection and variable construction as in Yatchew and No (2001) and Belloni et al.”
Non‐parametric methods for doubly robust estimation of continuous treatment effects (2015, methodology): “Rubin and van der Laan (2005, 2006a, b) proposed extensions to such paradigms in numerous important problems, but Rubin and van der Laan (2005, 2006a) considered weighted averages of dose–response curves and Rubin and van der Laan (2006b) did not consider doubly robust estimation.”
Towards optimal doubly robust estimation of heterogeneous causal effects (2020, methodology): “…formal nonparametric model where the CATE has its own complexity separate from regression functions seems to be Example 4 of Robins et al. [2008]. van der Laan [2005] (Section 4.2) proposed an important model-free “meta-algorithm” for estimating the CATE, a variant of which we study in this…”