Augmenting Pre-Analysis Plans with Machine Learning

Jens Ludwig, Sendhil Mullainathan and Jann Spiess

AEA Papers and Proceedings, 2019, vol. 109, 71-76

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.

JEL-codes: C45 (search for similar items in EconPapers)
Date: 2019
Note: DOI: 10.1257/pandp.20191070
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Citations: View citations in EconPapers (4)

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