Conformal Prediction for Nonparametric Instrumental Regression
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915892700381184 |
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| author | Kato, Masahiro |
| author_facet | Kato, Masahiro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_25509 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction for Nonparametric Instrumental Regression Kato, Masahiro Econometrics Machine Learning Applications Methodology 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. |
| title | Conformal Prediction for Nonparametric Instrumental Regression |
| topic | Econometrics Machine Learning Applications Methodology |
| url | https://arxiv.org/abs/2603.25509 |