David M. Ritzwoller, Vasilis Syrgkanis
arXiv 13 May 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2405.07860 · PDF · DOI · OpenAlex · Extracted main text
We construct simultaneous confidence intervals for solutions to conditional moment equations. The intervals are built around a class of nonparametric regression algorithms based on subsampled kernels. This class encompasses various forms of subsampled random forest regression, including Generalized Random Forests (Athey et al., 2019). Although simultaneous validity is often desirable in practice -- for example, for fine-grained characterization of treatment effect heterogeneity -- only confidence intervals that confer pointwise guarantees were previously available. Our work closes this gap. As a by-product, we obtain several new order-explicit results on the concentration and normal approximation of high-dimensional U-statistics.
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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 | Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parient… (2015) A multifaceted program causes lasting progress for the very poor: Evidence from six countries | 1.000 | 33 | 5 | 100% |
| 2 | Song, Y., Chen, X., and Kato, K (2019) Approximating high-dimensional infinite-order $u$-statistics: Statistical and computational guarantees | 1.000 | 17 | 5 | 100% |
| 3 | Wager, S. and Athey, S (2018) Estimation and inference of heterogeneous treatment effects using random forests | 1.000 | 10 | 5 | 100% |
| 4 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning | 1.000 | 10 | 3 | 100% |
| 5 | Minsker, S (2023) U-statistics of growing order and sub-gaussian mean estimators with sharp constants | 1.000 | 9 | 4 | 100% |
| 6 | Oprescu, M., Syrgkanis, V., and Wu, Z. S (2019) Orthogonal random forest for causal inference self | 1.000 | 9 | 4 | 100% |
| 7 | Athey, S., Tibshirani, J., and Wager, S (2019) Generalized random forests | 1.000 | 8 | 4 | 100% |
| 8 | Chernozhukov, V., Chetverikov, D., Kato, K., and Koike, Y (2022) Improved central limit theorem and bootstrap approximations in high dimensions | 1.000 | 7 | 3 | 100% |
| 9 | Chen, Q., Syrgkanis, V., and Austern, M (2022) Debiased machine learning without sample-splitting for stable estimators self | 1.000 | 6 | 3 | 100% |
| 10 | De la Pena, V. and Giné, E (1999) Decoupling: from dependence to independence | 1.000 | 5 | 3 | 100% |
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