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Producing Policy Recommendations: from Statistical Decision Theory to Empirical Practice

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  • Giacomo Opocher

Abstract

Applied research in economics is intrinsically motivated by broad normative objectives. However, it is not obvious how a researcher should direct their efforts to produce evidence toward such objectives. This paper reviews recent theoretical developments on research design for policy choice and provides new tools applied researchers can use to guide their design choices and communicate their policy recommendations. First, I focus on theoretical contributions in econometrics and provide a general framework that nests all the contexts and results reviewed using a coherent notation and narrative. Then, I present two diagrams applied researchers can use to navigate the theoretical literature starting from concrete scenarios to make thoughtful design choices. Finally, I introduce a new R package that produces one table and two figures applied researchers can plug in their `policy implications' section to provide evidence on the performance of different policy recommendations coming out of their study. The use of such tools is illustrated with an example in development economics.

Suggested Citation

  • Giacomo Opocher, 2026. "Producing Policy Recommendations: from Statistical Decision Theory to Empirical Practice," Papers 2607.29281, arXiv.org, revised Aug 2026.
  • Handle: RePEc:arx:papers:2607.29281
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    File URL: https://arxiv.org/pdf/2607.29281
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    References listed on IDEAS

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    1. Shosei Sakaguchi, 2025. "Estimation of optimal dynamic treatment assignment rules under policy constraints," Quantitative Economics, Econometric Society, vol. 16(3), pages 981-1022, July.
    2. Giacomo Opocher, 2026. "Better Measurement or Larger Samples? Data Collection for Policy Learning with Unobserved Heterogeneity," Papers 2604.07181, arXiv.org, revised May 2026.
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    Cited by:

    1. Harvey Barnhard & Giacomo Opocher & Rahul Singh, 2026. "Stable Policy Learning," Papers 2609.19418, arXiv.org.

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