Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
arXiv 3 Jun 2022 · Econometrics · 4 citations (OpenAlex)
arXiv:2206.01825 · PDF · DOI · OpenAlex · Extracted main text
Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line of work on debiased machine learning shows how one can use generic machine learning estimators for these auxiliary problems, while maintaining asymptotic normality and root-$n$ consistency of the target parameter of interest, while only requiring mean-squared-error guarantees from the auxiliary estimation algorithms. The literature typically requires that these auxiliary problems are fitted on a separate sample or in a cross-fitting manner. We show that when these auxiliary estimation algorithms satisfy natural leave-one-out stability properties, then sample splitting is not required. This allows for sample re-use, which can be beneficial in moderately sized sample regimes. For instance, we show that the stability properties that we propose are satisfied for ensemble bagged estimators, built via sub-sampling without replacement, a popular technique in machine learning practice.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Victor Chernozhukov, Whitney Newey, Rahul Singh, and Vasilis Syrgkanis (2020) Adversarial estimation of riesz representers self | 0.874 | 5 | 2 | 100% |
| 2 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2021) A simple and general debiased machine learning theorem with finite sample guarantees | 0.843 | 3 | 3 | 100% |
| 3 | Morgane Austern and Wenda Zhou (2020) Asymptotics of cross-validation self | 0.644 | 2 | 2 | 100% |
| 4 | Pierre Bayle, Alexandre Bayle, Lucas Janson, and Lester Mackey (2020) Cross-validation confidence intervals for test error | 0.644 | 2 | 2 | 100% |
| 5 | Olivier Bousquet and André Elisseeff (2002) Stability and generalization | 0.644 | 2 | 2 | 100% |
| 6 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning | 0.585 | 3 | 1 | 100% |
| 7 | Khashayar Khosravi, Greg Lewis, and Vasilis Syrgkanis (2019) Non-parametric inference adaptive to intrinsic dimension self | 0.511 | 2 | 2 | 50% |
| 8 | Peter J Bickel and Yaacov Ritov (1988) Estimating integrated squared density derivatives: Sharp best order of convergence estimates | 0.511 | 2 | 1 | 100% |
| 9 | Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whit… (2016) Locally robust semiparametric estimation | 0.511 | 2 | 1 | 100% |
| 10 | Rafail Z Hasminskii and Ildar A Ibragimov (1979) On the nonparametric estimation of functionals | 0.511 | 2 | 1 | 100% |
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