Victor Chernozhukov, Whitney Newey, Rahul Singh
arXiv 23 Feb 2018 · Statistics — Machine Learning · 26 citations (OpenAlex)
arXiv:1802.08667 · PDF · DOI · OpenAlex · Extracted main text
We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak “double sparsity robustness”: either the approximation to the regression or the approximation to the representer can be “completely dense” as long as the other is sufficiently “sparse”. Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.
appendix boundary found by appendix_command · 58% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.969 | 11 | 5 | 91% |
| 2 | Chernozhukov, V., W. K. Newey, and R. Singh (2018) Learning L2 continuous regression functionals via regularized Riesz representers self | 0.950 | 7 | 4 | 86% |
| 3 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2016) Locally robust semiparametric estimation self | 0.874 | 6 | 3 | 67% |
| 4 | Belloni, A., V. Chernozhukov, and L. Wang (2014) Pivotal estimation via square-root lasso in nonparametric regression | 0.874 | 5 | 2 | 100% |
| 5 | Semenova, V. and V. Chernozhukov (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.843 | 5 | 3 | 60% |
| 6 | Van der Vaart, A. W (2000) Asymptotic Statistics, Volume 3 | 0.843 | 5 | 3 | 60% |
| 7 | Foster, D. J. and V. Syrgkanis (2019) Orthogonal statistical learning | 0.737 | 3 | 3 | 67% |
| Chernozhukov | unmatched citation key Chernozhukov | 0.693 | 14 | 1 | 100% |
| Newey | unmatched citation key Newey | 0.693 | 10 | 1 | 100% |
| Belloni | unmatched citation key Belloni | 0.693 | 5 | 1 | 100% |
Showing the top 10 of 218 scored citations. 3 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.