Conformal Prediction for Nonparametric Instrumental Regression
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.
| Authors | Masahiro Kato |
|---|---|
| Primary category | econ.EM |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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