Conformal Prediction for Nonparametric Instrumental Regression
This addresses the need for reliable uncertainty quantification in causal inference settings, particularly for practitioners using NPIV methods, though it is incremental as it builds on existing conformal inference frameworks.
The paper tackles the problem of constructing prediction intervals with finite-sample coverage guarantees in nonparametric instrumental variable regression, achieving distribution-free coverage over a class of IV shifts.
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.