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Conformal Prediction for Nonparametric Instrumental Regression

arXiv 2603.25509 ↗First posted 26 Mar 2026econ.EM cs.LG stat.APcited by 0

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Abstract (arXiv)

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 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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References · 16

  1. 2019Conformalized Quantile Regressioncited by 625
  2. 2019Conformal Prediction Under Covariate Shiftcited by 513
  3. 2023Conformal Prediction With Conditional Guaranteescited by 150
  4. 2019Kernel Instrumental Variable Regressioncited by 118
  5. 2021Localized Conformal Prediction: A Generalized Inference Framework for Conformal Predictioncited by 113
  6. 2019Distributional conformal predictioncited by 105
  7. 2019Deep Generalized Method of Moments for Instrumental Variable Analysiscited by 88
  8. 2020Minimax Estimation of Conditional Moment Modelscited by 83
  9. 2022Batch Multivalid Conformal Predictioncited by 66
  10. 2020Learning Deep Features in Instrumental Variable Regressioncited by 65
  11. 2022Practical Adversarial Multivalid Conformal Predictioncited by 65
  12. 2018Adversarial Generalized Method of Momentscited by 34
4 more
  1. 2020Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approachcited by 31
  2. 2015Optimal Sup-norm Rates and Uniform Inference on Nonlinear Functionals of Nonparametric IV Regressioncited by 30
  3. 2020The Variational Method of Momentscited by 23
  4. 2021Learning Causal Models from Conditional Moment Restrictions by Importance Weightingcited by 7

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