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.

该奖项将支持开发政策评估创新方法的研究。 广泛采用实验和观察数据来评估政策变化已经彻底改变了二十世纪的政策制定。然而，政策制定者在做出数据驱动的政策选择时面临一些复杂的情况。例如，可能会担心实验结果是否具有普遍性，或者实验可能具有不完美的合规性。这些问题意味着现有数据仅提供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来开发新决策规则的研究，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。待开发的方法易于数学求解，并允许研究人员在制定政策决策时充分利用可用数据。新方法将在经济学、计量经济学、生物统计学和其他社会科学的多个领域发挥作用。研究人员将编写统计代码来实施新方法。 除了改善经济科学外，该奖项支持的研究成果还将改善政策设计，从而改善经济表现和美国公民的福祉。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计量经济学联系起来开发新的估计和推理方法的研究。 第一个项目研究二元政策选择问题，其中部分确定了与收益相关的参数。一个关键的研究成果将是一种新颖的决策规则，该规则明确使用已识别的收益相关参数集的估计量。该规则将通过一种新的有限样本最优理论来证明，该理论改进了极小最大遗憾原则。第二个项目涉及政策制定者，他不确定真正的政策效果，但可以访问随机对照试验生成的数据，但合规性不完全。该研究将对通常的“IV”估计量给出决策理论解释，无论它是否承认因果解释。研究人员将应用这些结果，通过实验和观察数据来研究数据驱动的政策制定。这项研究的结果将改进数据驱动的政策决策方法。 除了改善经济科学之外，该奖项支持的研究成果还将改善政策设计，从而改善经济表现和美国公民的福祉。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。