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Selecting Experimental Sites for External Validity

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arxiv 2405.13241 v1 pith:QGJZ3QZX submitted 2024-05-21 econ.GN q-fin.EC

Michael Gechter , Keisuke Hirano , Jean Lee , Mahreen Mahmud , Orville Mondal , Jonathan Morduch , Saravana Ravindran , Abu S. Shonchoy This is my paper · ORCID
classification econ.GNq-fin.EC
keywords sitesexternalvalidityeffectsevidencepolicytreatmentallowing
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Policy decisions often depend on evidence generated elsewhere. We take a Bayesian decision-theoretic approach to choosing where to experiment to optimize external validity. We frame external validity through a policy lens, developing a prior specification for the joint distribution of site-level treatment effects using a microeconometric structural model and allowing for other sources of heterogeneity. With data from South Asia, we show that, relative to basing policies on experiments in optimal sites, large efficiency losses result from instead using evidence from randomly-selected sites or, conversely, from sites with the largest expected treatment effects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dynamically Consistent Statistical Decisions

    econ.EM 2026-07 conditional novelty 7.0 of 10

    Frequentist minimax rules often lack interim credibility; two axiomatized dynamically consistent criteria restore it while nesting Manski as-if and Gamma*-minimax.

  2. Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence

    econ.EM 2025-10 conditional novelty 7.0 of 10

    Designing experiments that will be combined with observational evidence reduces to balancing a normalized variance regret against a normalized bias regret.

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