决策可承受性的几何控制
Learning Where to Look: Delaunay Matching for Policy Choice and Data Collection
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中文总结 AI 辅助
从几何角度研究数据驱动决策性能，引入认证概念，证明其与最坏情况补偿的关系，研究匹配估计量，表明大样本下认证可承受性成几何问题，还展示如何指导捐赠数据收集计划。
中文摘要 AI 辅助
本文从几何角度研究数据驱动决策的性能。政策制定者从创新捐赠人群中学习，以决定是否对不同目标人群中的群体进行创新，并必须补偿任何错误。我引入认证：当干预效应在幅度上足够大时，估计器在控制错误概率时产生认证决策。首先，我表明认证意味着最坏情况补偿的界限。然后，我研究具有正权重的匹配估计器，并表明，在大样本情况下，通过认证的可承受性变成一个纯粹的几何问题。我证明，从计算几何结果中已知其性质的德劳内插值器提供了最佳的可承受性保证。最后，我展示了如何利用这一结果来指导捐赠数据收集计划，以使最坏情况补偿成本低于目标水平。我在发展经济学的半合成实证应用中说明了在目标设定和收集计划中采用这种几何观点的好处。
英文摘要
This paper studies data-driven policy choices from a geometric perspective. A sample from a donor population fully exposed to an innovation informs a policymaker (PM) on whether to innovate groups in a target population where the innovation was not introduced. Any wrong decision must be compensated from a finite budget and the PM seeks an estimator of the innovation's effect that guarantees an affordable compensation cost. I focus on matching estimators with positive weights and derive affordability guarantees when finite and large samples of the populations of interest are available. In the latter case, Delaunay interpolants, whose properties are well-known from results in computational geometry, deliver the smallest budget that covers the compensation cost uniformly over the admissible target populations, and conditional on the donor collection design. This result informs where to look for new donor observations to decrease the worst-case compensation cost the most. In an empirical application, I show that such collection plans halve the cost by adding three donor units, while random sampling fails to reach the same target within twenty additions.
