Mostly Harmless Statistical Decision Theory

该奖项将支持开发政策评估创新方法的研究。广泛采用实验和观测数据来评估政策变化，彻底改变了20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着一些复杂的问题。例如，可能会担心实验的结果是否可以推广，或者实验是否具有不完美的依从性。这些问题意味着，现有的数据只能提供与一项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制定新的决策规则的研究，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。要开发的方法很容易在数学上解决，并且允许研究人员在制定政策决策时最大限度地利用现有数据。这些新方法将在经济学、计量经济学、生物统计学和其他社会科学的几个领域发挥作用。研究人员将编写一个统计代码来实现这些新方法。除了改善经济科学，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的研究，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。第一个课题研究了一个收益相关参数部分确定的二元策略选择问题。一个关键的研究成果将是一个新的决策规则，它明确地使用了收益相关参数的识别集的估计器。该规则将被一个新的有限样本最优理论所证明，该理论改进了极大极小后悔原则。第二个项目涉及一位政策制定者，他不确定真正的政策效果，但他可以获得一项不完全合规的随机对照试验生成的数据。该研究将给出通常的“IV”估计量的决策理论解释，而不管它是否允许因果解释。研究人员将把这些结果应用于利用实验和观测数据研究数据驱动的政策制定。本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，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.