arXiv 19 Apr 2026 · Mathematics — Statistics Theory
arXiv:2604.17239 · PDF · DOI · OpenAlex · Extracted main text
Double/debiased machine learning (DML) provides a general framework for inference with high-dimensional or otherwise complex nuisance parameters by combining Neyman-orthogonal scores with cross-fitting, thereby circumventing classical Donsker-type conditions in many modern machine-learning settings. Despite its strong empirical performance, bootstrap inference for DML estimators has received little theoretical justification. This is particularly noteworthy since bootstrap methods are suggested ad used for inference on DML estimators, even though bootstrap procedures can fail for estimators that are root-$n$ consistent and asymptotically normal. This paper fills this gap by establishing bootstrap validity for DML estimators under general exchangeably weighted resampling schemes, with Efron's bootstrap as a special case. Under exactly the same conditions required for the validity of DML itself, we prove that the bootstrap law converges conditionally weakly to the sampling law of the original estimator.
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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., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 8 | 3 | 100% |
| 2 | Praestgaard, J. and Wellner, J. A (1993) Exchangeably weighted bootstraps of the general empirical process | 1.000 | 6 | 4 | 100% |
| 3 | Cheng, G. and Huang, J. Z (2010) Bootstrap consistency for general semiparametric M-estimation | 0.644 | 2 | 2 | 100% |
| 4 | Efron, B (1979) Bootstrap methods: Another look at the jackknife | 0.644 | 2 | 2 | 100% |
| 5 | Abadie, A. and Imbens, G. W (2008) On the failure of the bootstrap for matching estimators | 0.405 | 1 | 1 | 100% |
| 6 | Andrews, D. W (1994) Empirical process methods in econometrics | 0.405 | 1 | 1 | 100% |
| 7 | Beran, R (1987) Prepivoting to reduce level error of confidence sets | 0.405 | 1 | 1 | 100% |
| 8 | Cai, W. and van der Laan, M (2020) Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (lasso) estim… | 0.405 | 1 | 1 | 100% |
| 9 | Chernozhukov, V., Chetverikov, D., and Kato, K (2014) Gaussian approximation of suprema of empirical processes | 0.405 | 1 | 1 | 100% |
| 10 | Diciccio, T. J. and Romano, J. P (1988) A review of bootstrap confidence intervals | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.