基于多个研究设计的标题估计

中文摘要 AI 辅助

为研究一个标量参数，研究者可能会考虑多个研究设计。基于不同设计的证据，研究者希望得出该参数的标题估计值。本文探讨当不清楚哪个设计最适合研究该参数时，如何选择此标题估计值。本文对该场景进行建模，假设：（i）这些设计中恰好有一个对该参数是有效的；（ii）研究者对哪个设计有效存在模糊性，这种模糊性由候选设计上的一类先验表示。为解决模糊性问题，本文建议报告使该类先验下最坏情况后验风险最小的标题估计值。在三个应用案例中，本文展示了考虑模糊性会实质性影响定量结论的情况，以及现有标题估计值已接近最优的情况。

英文摘要

To study a scalar parameter, a researcher may consider multiple research designs. Based on the evidence across designs, the researcher may wish to formulate a headline estimate of the parameter. I examine how to choose this headline when it is unclear which design is most appropriate for studying the parameter. I model this setting by assuming that (i) exactly one of the designs is valid for the parameter and (ii) the researcher has ambiguity about which design is valid, represented by a class of priors over the candidate designs. To account for ambiguity, I propose reporting the headline estimate that minimizes the worst-case posterior risk over the class of priors. In three applications, I show cases where accounting for ambiguity materially affects the quantitative conclusion and cases where an existing headline is already close to optimal.

发表机构

• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。