Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
arXiv 30 Dec 2020 · Econometrics · publishedJournal of the American Statistical Association (2025) · 5 citations (OpenAlex)
arXiv:2101.00009 · PDF · DOI · OpenAlex · Extracted main text
Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators achieve nominal coverage in highly nonlinear simulations where some previous methods break down. They shed new light on the heterogeneous effects of matching grants.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Karlan, D. and List, J. A (2007) Does price matter in charitable giving? evidence from a large-scale natural field experiment | 0.961 | 9 | 3 | 89% |
| 2 | Wainwright, M. J (2019) High-dimensional statistics: A non-asymptotic viewpoint, volume 48 | 0.928 | 5 | 3 | 80% |
| 3 | Hirshberg, D. A. and Wager, S (2021) Augmented minimax linear estimation | 0.843 | 5 | 4 | 60% |
| 4 | Dikkala, N., Lewis, G., Mackey, L., and Syrgkanis, V (2020) Minimax estimation of conditional moment models self | 0.763 | 9 | 4 | 44% |
| 5 | Shalev-Shwartz, S. and Ben-David, S (2014) Understanding machine learning: From theory to algorithms | 0.737 | 5 | 3 | 40% |
| 6 | Chernozhukov, V., Newey, W. K., and Singh, R (2022) Automatic debiased machine learning of causal and structural effects self | 0.737 | 4 | 4 | 50% |
| 7 | Chernozhukov, V., Newey, W. K., and Singh, R (2022) De-biased machine learning of global and local parameters using regularized Riesz representers self | 0.737 | 4 | 3 | 50% |
| 8 | Foster, D. J. and Syrgkanis, V (2023) Orthogonal statistical learning self | 0.737 | 4 | 3 | 50% |
| 9 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.737 | 3 | 2 | 100% |
| 10 | Zheng, W. and van der Laan, M. J (2011) Cross-validated targeted minimum-loss-based estimation | 0.737 | 3 | 2 | 100% |
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