arXiv 26 Mar 2026 · Econometrics
arXiv:2603.25509 · PDF · DOI · OpenAlex · Extracted main text
We propose a method for constructing distribution-free prediction intervals in nonparametric instrumental variable regression (NPIV), with finite-sample coverage guarantees. Building on the conditional guarantee framework in conformal inference, we reformulate conditional coverage as marginal coverage over a class of IV shifts $\mathcal{F}$. Our method can be combined with any NPIV estimator, including sieve 2SLS and other machine-learning-based NPIV methods such as neural networks minimax approaches. Our theoretical analysis establishes distribution-free, finite-sample coverage over a practitioner-chosen class of IV shifts.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, and… (2022) Learning causal models from conditional moment restrictions by importance weighting self | 0.941 | 6 | 4 | 83% |
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| 3 | Jing Lei and Larry Wasserman (2013) Distribution-free prediction bands for non-parametric regression | 0.928 | 5 | 4 | 80% |
| 4 | Isaac Gibbs, John J Cherian, and Emmanuel J Candès (2025) Conformal prediction with conditional guarantees | 0.920 | 9 | 5 | 78% |
| 5 | Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya… (2019) Conformal prediction under covariate shift | 0.909 | 8 | 4 | 75% |
| 6 | Chunrong Ai and Xiaohong Chen (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.843 | 4 | 3 | 75% |
| 7 | Serge Darolles, Yanqin Fan, Jean-Pierre Florens, and Eric Renault (2011) Nonparametric instrumental regression | 0.843 | 4 | 3 | 75% |
| 8 | Nishanth Dikkala, Greg Lewis, Lester Mackey, and Vasilis Syrgkanis (2020) Minimax estimation of conditional moment models | 0.843 | 5 | 4 | 60% |
| 9 | Whitney K. Newey and James L. Powell (2003) Instrumental variable estimation of nonparametric models | 0.737 | 4 | 2 | 75% |
| 10 | Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy (2017) Deep IV: A flexible approach for counterfactual prediction | 0.644 | 3 | 2 | 67% |
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