Response to comment on Policy impacts of statistical uncertainty and privacy
成果类型：
Editorial Material
署名作者：
Steed, Ryan; Acquisti, Alessandro; Wu, Zhiwei Steven; Liu, Terrance
署名单位：
Carnegie Mellon University
刊物名称：
SCIENCE
ISSN/ISSBN：
0036-11897
DOI：
10.1126/science.adh2297
发表日期：
2023-06-02
关键词：
摘要：
We offer our thanks to the authors for their thoughtful comments. Cui, Gong, Hannig, and Hoffman propose a valuable improvement to our method of estimating lost entitlements due to data error. Because we don't have access to the unknown, true number of children in poverty, our paper simulates data error by drawing counterfactual estimates from a normal distribution around the official, published poverty estimates, which we use to calculate lost entitlements relative to the official allocation of funds. But, if we make the more realistic assumption that the published estimates are themselves normally distributed around the true number of children in poverty, Cui et al.'s proposed framework allows us to reliably estimate lost entitlements relative to the unknown, ideal allocation of funds-what districts would have received if we knew the true number of children in poverty.