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Prediction, Estimation, and Attribution

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  • Bradley Efron

Abstract

The scientific needs and computational limitations of the twentieth century fashioned classical statistical methodology. Both the needs and limitations have changed in the twenty-first, and so has the methodology. Large-scale prediction algorithms—neural nets, deep learning, boosting, support vector machines, random forests—have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic datasets. How do these algorithms compare with standard regression techniques such as ordinary least squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (significance testing). Most of the discussion is carried out through small numerical examples.

Suggested Citation

  • Bradley Efron, 2020. "Prediction, Estimation, and Attribution," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 636-655, April.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:530:p:636-655
    DOI: 10.1080/01621459.2020.1762613
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    Cited by:

    1. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
    2. M. Merz & R. Richman & T. Tsanakas & M. V. Wuthrich, 2021. "Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles," Papers 2103.11706, arXiv.org.
    3. Denis A Shah & Erick D De Wolf & Pierce A Paul & Laurence V Madden, 2021. "Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-23, March.
    4. Ord, J. Keith, 2022. "The uncertainty track: Machine learning, statistical modeling, synthesis," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1526-1530.
    5. Nelson P. Rayl & Nitish R. Sinha, 2022. "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series 2022-042, Board of Governors of the Federal Reserve System (U.S.).
    6. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    7. COJOCARIU Irina-Cristina, 2023. "Analysis Of Sports Performances Using Machine Learning And Statistical Models - A General Analysis Of The Literature," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 75(2), pages 34-39, June.
    8. Chun Chieh Fan & Robert Loughnan & Carolina Makowski & Diliana Pecheva & Chi-Hua Chen & Donald J. Hagler & Wesley K. Thompson & Nadine Parker & Dennis van der Meer & Oleksandr Frei & Ole A. Andreassen, 2022. "Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    9. Takahiro Yoshida & Daisuke Murakami & Hajime Seya, 2024. "Spatial Prediction of Apartment Rent using Regression-Based and Machine Learning-Based Approaches with a Large Dataset," The Journal of Real Estate Finance and Economics, Springer, vol. 69(1), pages 1-28, July.
    10. Jeff Dominitz & Charles F. Manski, 2024. "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," Papers 2403.11016, arXiv.org, revised May 2024.
    11. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
    12. Benítez-Peña, Sandra & Carrizosa, Emilio & Guerrero, Vanesa & Jiménez-Gamero, M. Dolores & Martín-Barragán, Belén & Molero-Río, Cristina & Ramírez-Cobo, Pepa & Romero Morales, Dolores & Sillero-Denami, 2021. "On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19," European Journal of Operational Research, Elsevier, vol. 295(2), pages 648-663.
    13. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.
    14. Rich, Jeppe & Myhrmann, Marcus Skyum & Mabit, Stefan Eriksen, 2023. "Our children cycle less - A Danish pseudo-panel analysis," Journal of Transport Geography, Elsevier, vol. 106(C).
    15. Anna Gottard & Giulia Vannucci & Leonardo Grilli & Carla Rampichini, 2023. "Mixed-effect models with trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 431-461, June.
    16. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
    17. Weishampel, Anthony & Staicu, Ana-Maria & Rand, William, 2023. "Classification of social media users with generalized functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).

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