Victor Chernozhukov, Whitney K. Newey, Rahul Singh
arXiv 31 May 2021 · Statistics — Machine Learning · publishedBiometrika (2022) · 19 citations (OpenAlex)
arXiv:2105.15197 · PDF · DOI · OpenAlex · Extracted main text
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e. scalar summaries, of machine learning algorithms. For example, an analyst may desire the confidence interval for a treatment effect estimated with a neural network. We provide a nonasymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable conditions. Formally, we prove consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments. The rate of convergence is $n^{-1/2}$ for global functionals, and it degrades gracefully for local functionals. Our results culminate in a simple set of conditions that an analyst can use to translate modern learning theory rates into traditional statistical inference. The conditions reveal a general double robustness property for ill posed inverse problems.
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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 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.928 | 5 | 3 | 80% |
| 2 | Nathan Kallus, Xiaojie Mao, and Masatoshi Uehara (2021) Causal inference under unmeasured confounding with negative controls: A minimax learning approach | 0.737 | 3 | 3 | 67% |
| 3 | Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whit… (2016) Locally robust semiparametric estimation self | 0.737 | 3 | 2 | 100% |
| 4 | Victor Chernozhukov, Whitney Newey, and Rahul Singh (2018) Debiased machine learning of global and local parameters using regularized Riesz representers self | 0.721 | 8 | 4 | 38% |
| 5 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2018) Automatic debiased machine learning of causal and structural effects self | 0.644 | 4 | 2 | 50% |
| 6 | Rahul Singh, Maneesh Sahani, and Arthur Gretton (2019) Kernel instrumental variable regression self | 0.644 | 2 | 2 | 100% |
| 7 | Rahul Singh (2021) Debiased kernel methods self | 0.644 | 2 | 2 | 100% |
| 8 | Jason Abrevaya, Yu-Chin Hsu, and Robert P Lieli (2015) Estimating conditional average treatment effects | 0.585 | 3 | 3 | 33% |
| 9 | Kyle Colangelo and Ying-Ying Lee (2020) Double debiased machine learning nonparametric inference with continuous treatments | 0.511 | 2 | 2 | 50% |
| 10 | Hidehiko Ichimura and Whitney K Newey (2021) The influence function of semiparametric estimators self | 0.511 | 2 | 2 | 50% |
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