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Augmenting Pre-Analysis Plans with Machine Learning

Author

Listed:
  • Jens Ludwig
  • Sendhil Mullainathan
  • Jann Spiess

Abstract

Concerns about the dissemination of spurious results have led to calls for pre-analysis plans (PAPs) to avoid ex-post "p-hacking." But often the conceptual hypotheses being tested do not imply the level of specificity required for a PAP. In this paper we suggest a framework for PAPs that capitalize on the availability of causal machine-learning (ML) techniques, in which researchers combine specific aspects of the analysis with ML for the flexible estimation of unspecific remainders. A "cheap-lunch" result shows that the inclusion of ML produces limited worst-case costs in power, while offering a substantial upside from systematic specification searches.

Suggested Citation

  • Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2019. "Augmenting Pre-Analysis Plans with Machine Learning," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 71-76, May.
    • Handle: RePEc:aea:apandp:v:109:y:2019:p:71-76
    Note: DOI: 10.1257/pandp.20191070
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    Cited by:

    1. Brodeur, Abel & Cook, Nikolai M. & Hartley, Jonathan S. & Heyes, Anthony, 2022. "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," GLO Discussion Paper Series 1147, Global Labor Organization (GLO).
    2. Edward Miguel, 2021. "Evidence on Research Transparency in Economics," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 193-214, Summer.
    3. Avdeenko, Alexandra & Frölich, Markus, 2020. "Research standards in empirical development economics: What’s well begun, is half done," World Development, Elsevier, vol. 127(C).
    4. Susanna Loeb & Michala Iben Riis-Vestergaard & Marianne Simonsen, 2023. "Supporting Language Development through a Texting Program: Initial Results from Denmark," Economics Working Papers 2023-01, Department of Economics and Business Economics, Aarhus University.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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