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An introduction to conformal inference for economists

Author

Listed:
  • Paul, Joseph R.
  • Schaffer, Mark E.

Abstract

This paper introduces conformal inference, a powerful and flexible framework for constructing prediction intervals with guaranteed coverage in finite samples. Unlike conventional methods, conformal inference makes no assumptions about the underlying data distribution other than exchangeability. The paper begins with some simple examples of full and split conformal prediction that highlight the key assumption of exchangeability. We then provide more formal treatments of full and split conformal prediction along with extensions of the basic framework, including the Jackknife+ and CV+ algorithms, both of which offer a better balance between computational and statistical efficiency compared to full and split conformal prediction. The paper then discusses the limitations to achieving exact conditional coverage and several methods that aim to improve conditional coverage in practice. The final section briefly discusses areas of current research the software options for implementing conformal methods.

Suggested Citation

  • Paul, Joseph R. & Schaffer, Mark E., 2024. "An introduction to conformal inference for economists," Accountancy, Economics, and Finance Working Papers 2024-13, Heriot-Watt University, Department of Accountancy, Economics, and Finance.
    • Handle: RePEc:zbw:hwuaef:308058
    as

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    References listed on IDEAS

    as
    1. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    2. Jing Lei & Larry Wasserman, 2014. "Distribution-free prediction bands for non-parametric regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 71-96, January.
    3. Leying Guan, 2023. "Localized conformal prediction: a generalized inference framework for conformal prediction," Biometrika, Biometrika Trust, vol. 110(1), pages 33-50.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    conformal inference; conformal prediction; distribution-free inference; machine learning;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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