Double/de-biased machine learning using regularized Riesz representers

By Victor Chernozhukov, Whitney K. Newey, James M. Robins. 2018

65 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

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.”
Locally Robust Semiparametric Estimation (2016, methodology): “Here we focus on debiased GMM. Recent work on debiased machine learning by Chernozhukov et al. (2018), Chernozhukov, Newey, and Robins (2018), and Chernozhukov, Newey, and Singh (2018) is partly based on and is also generalized by this paper.”
Policy Learning With Observational Data (2017, methodology): “…to their ability to achieve semiparametric eﬃciency under general conditions, even if nuisance components are estimated via black-box machine learning methods for which we can only guarantee fast enough convergence in mean-squared error (Chernozhukov et al., 2018a; van der Laan and Rose, 2011).”
The Balancing Act in Causal Inference (2021, background): “Taking the dual perspective, Chernozhukov, Newey, and Robins (2018) proposed these weights as an ℓ1-penalized least squares estimator for the inverse propensity score.”
High-dimensional econometrics and regularized GMM (2018, background): “The advantage of [56]’s approach over that in [4] is that the former explicitly allows the tradeoff in the rate of estimating the inverse propensity score with the rate of estimating the regression function, allowing misspecification of both functions.”
Debiased Inference of Average Partial Effects in Single-Index Models (2018, methodology): “…and then debias the plug-in estimator for τj based on θ̂ using a weighted average of residuals:1 τ̂j = 1 n n∑ i=1 ( θ̂jψ ( XTi θ̂ ) + γ̂i ( Yi −Ψ ( XTi θ̂ ))) , (4) 1Our proposed estimator will also use cross-fitting [Chernozhukov et al., 2018a], but we suppress this notation here for conciseness.”
Incremental causal effects (2019, methodology): “In Hirshberg and Wager (2017), this is achieved under Donsker assumptions, while Chernozhukov et al. (2018) employ a sparsity assumption on either the approximation of the Riesz representer or the approximation to the regression function.”
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.”
Augmented minimax linear estimation (2017, methodology): “This phenomenon is not unique to our approach, as some other methods can estimate something like a Riesz representer on a tangent space of their choosing; see, e.g., Remark 2.5 of Chernozhukov et al. (2018a) or Section 3 of Robins et al. (2007).”
Learning L2 Continuous Regression Functionals via Regularized Riesz Representers (2018, methodology): “Proof of Theorem 10: We prove the first conclusion by verifying the conditions of Lemma 14 of Chernozhukov et al. (2018b). Let λ in Chernozhukov et al.”