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Estimating the Value of Evidence-Based Decision Making

Author

Listed:
  • Alberto Abadie
  • Anish Agarwal
  • Guido Imbens
  • Siwei Jia
  • James McQueen
  • Serguei Stepaniants
  • Santiago Torres

Abstract

In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidence-based decision making (EBDM), optimize statistical precision, and balance the costs of evidence-gathering strategies against their benefits. To tackle these challenges, this article introduces an empirical framework to estimate the value of EBDM and evaluate the return on investment in statistical precision and project ideation. The framework leverages parametric and nonparametric empirical Bayes methods to account for parameter heterogeneity and measure how statistical precision changes the value of evidence. The value extracted from statistical evidence depends critically on how organizations translate evidence into policy decisions. Commonly used decision rules based on statistical significance can leave substantial value unrealized and, in some cases, generate negative expected value.

Suggested Citation

  • Alberto Abadie & Anish Agarwal & Guido Imbens & Siwei Jia & James McQueen & Serguei Stepaniants & Santiago Torres, 2023. "Estimating the Value of Evidence-Based Decision Making," Papers 2306.13681, arXiv.org, revised Feb 2026.
    • Handle: RePEc:arx:papers:2306.13681
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    References listed on IDEAS

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    1. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
    2. Roger Koenker & Ivan Mizera, 2014. "Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 674-685, June.
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