Augmenting Pre-Analysis Plans with Machine Learning
AbstractConcerns 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.
CitationLudwig, Jens, Sendhil Mullainathan, and Jann Spiess. 2019. "Augmenting Pre-Analysis Plans with Machine Learning." AEA Papers and Proceedings, 109: 71-76. DOI: 10.1257/pandp.20191070
- C45 Neural Networks and Related Topics