Optimal Post-Hoc Theorizing

Andrew Y. Chen

No 242, I4R Discussion Paper Series from The Institute for Replication (I4R)

Abstract: For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades off a "Darwinian Learning" effect from theorizing first with a "Statistical Learning" effect from examining the data first. This short paper formalizes the tradeoff in a Bayesian model. In the modern era of mature economic theory and enormous datasets, I argue that post hoc theorizing is typically optimal.

Keywords: Publication Bias; Machine Learning; Predictivism vs Accommodation; HARKing (search for similar items in EconPapers)
JEL-codes: B41 C11 C18 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:i4rdps:242

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