Learning L2 Continuous Regression Functionals via Regularized Riesz Representers

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Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “Using closed-form solutions for Riesz representers in several examples, Chernozhukov et al. (2016, 2018a) defined DML estimators in high dimensional settings and established their good properties.”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, methodology): “Specifically, Chernozhukov et al. (2018) showed that ATE is an instance of a much larger class of functionals, which includes many parameters of interest in causal inference, and which can be expressed as a continuos and linear functional of the conditional mean of an outcome given covariates.”
Locally Robust Semiparametric Estimation (2016, methodology): “This estimator ̂() of 0( 0) generalizes that of Chernozhukov, Newey, and Singh (2018) to allow for a residual ( ()) other than − () and a general moment function.”