Mostly Harmless Statistical Decision Theory

基本无害的统计决策理论

• 批准号: 2315600
• 负责人: Joerg Stoye
• 金额: $ 58.17万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315600&HistoricalAwards=false
• 关键词: Mostly; Harmless; Statistical; Decision; Theory

This award will support research to develop innovative methods for policy evaluation. The wide adoption of experimental and observational data to evaluate policy changes has revolutionized policy making in the twentieth century. However, policy makers face several complications when making data-driven policy choices. For example, there may be concerns about whether the results from the experiments might be generalizable or the experiments may feature imperfect compliance. These problems imply that the available data only provide partial information about the welfare associated with a new policy. This award will support research that uses the framework of Statistical Decision Theory to develop new decision rules that can be used to make policy choices in these complicated, but realistic, situations. The methods to be developed are easy to solve mathematically and allow researchers to make the most out of the available data when making policy decisions. The new methods will be useful in several areas of economics, econometrics, biostatistics, and other social sciences. The researchers will write a statistical code to implement the new methods. Besides improving economic science, the results of the research supported by this award will improve policy design and thus improve economic performance and the wellbeing of US citizens. This award supports research that uses three projects to develop new methods of estimation and inference by connecting Statistical Decision Theory to economics and econometrics. The first project studies a binary policy choice problem in which payoff relevant parameters are partially identified. A key research output will be a novel decision rule that makes explicit use of an estimator of the identified set for the payoff relevant parameters. The rule will be justified by a new finite-sample optimality theory that refines the minimax regret principle. The second project concerns a policy maker uncertain about the true policy effect but has access to data generated by an RCT with imperfect compliance. The research will give a decision theoretic interpretation of the usual “IV” estimators, irrespective of whether it admits a causal interpretation. The researchers will apply these results to study data-driven policy making with experimental and observational data. The results of this research will improve methods of data-driven policy decision-making. Besides improving economic science, the results from the research supported by this award will improve policy design and thus improve economic performance and the wellbeing of US citizens.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将支持研究以开发创新的政策评估方法。实验数据和观察数据的广泛采用来评估政策变化已彻底改变了20世纪的政策制定。但是，决策者在做出数据驱动的政策选择时会面临几种并发症。例如，可能会担心实验的结果是否可以推广，或者实验可能具有不完美的依从性。这些问题表明，可用数据仅提供有关与新政策相关的福利的部分信息。该奖项将支持使用统计决策理论框架的研究来制定新的决策规则，这些规则可用于在这些复杂但现实的情况下做出政策选择。要开发的方法易于通过数学解决，并允许研究人员在做出政策决策时充分利用可用数据。新方法将在经济学，经济学，生物统计学和其他社会科学领域中有用。研究人员将编写一个统计代码以实现新方法。除了改善经济科学外，该奖项支持的研究结果还将改善政策设计，从而改善经济绩效和美国公民的福祉。该奖项支持使用三个项目通过将统计决策理论与经济学和经济学联系起来来开发估计和推断的新方法的研究。第一个项目研究了一个二进制政策选择问题，其中部分确定了相关参数。关键的研究输出将是一项新的决策规则，该规则明确地使用了有关收益相关参数的已确定集合的估计器。该规则将通过一种新的有限样本最优理论来证明，该理论完善了Minimax遗憾原则。第二个项目涉及政策制定者对真实策略效应的不确定，但可以访问由RCT生成的数据，并符合不完美。这项研究将对通常的“ IV”估计量进行决策理论解释，而不管它是否接受因果解释。研究人员将将这些结果应用于通过实验和观察数据研究数据驱动的政策制定。这项研究的结果将改善数据驱动的政策决策方法。除了改善经济科学外，该奖项支持的研究结果还将改善政策设计，从而改善经济绩效和美国公民的福祉。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来审查标准的评估，被认为是宝贵的支持。