New Developments in Methodology for Program Evaluation

项目评估方法的新进展

• 批准号: 2019432
• 负责人: Matias Cattaneo
• 金额: $ 46万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019432&HistoricalAwards=false
• 关键词: New; Developments; Methodology; Program; Evaluation

This research project will develop new statistical methods for program evaluation with the goal of improving their reliability and scope in real-life applications. Program evaluation methods are widely used in a variety of disciplines in the social, behavioral, and biomedical sciences. These methods are empirically employed to determine the causal effect of a policy, intervention, or treatment on some outcome of interest. For example, program evaluation methods allow policy makers to improve social programs or public health officials to better target the mitigation of infectious diseases. This project will develop new identification, estimation, and inference results for three modern program evaluation methods: binscatter methods, synthetic control methods, and regression discontinuity methods. The results of this research will provide researchers with new methodological tools to improve empirical work. Graduate students will be mentored and trained in the new methodologies and provided with first-hand research experience. General-purpose software will be developed to implement the new methods.This research project will provide new developments in mathematical statistics and econometrics. For binscatter methods, which are closely related to machine learning techniques, new uniform inference methods will be developed that build on strong approximation tools from probability theory. These results will provide valid confidence bands and hypothesis tests about shape restrictions for causal inference models with possibly heterogenous treatment effects. For synthetic control methods, novel prediction intervals will be developed using non-asymptotic probability concentration ideas from the high-dimensional statistical literature. Practical implementation of these prediction intervals based on the bootstrap also will be studied. For regression discontinuity designs, the project will develop new identification, estimation, inference, and falsification methods for settings with duration-type outcomes and allowing for covariate-adjustment and multiple cutoffs. Robust bias correction inference methods will be developed along with tuning parameter selection methods.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.

这项研究项目将开发新的统计方法来进行方案评估，目的是提高它们在实际应用中的可靠性和范围。项目评估方法被广泛应用于社会、行为和生物医学科学中的各种学科。这些方法被经验性地用来确定政策、干预或治疗对某些感兴趣的结果的因果影响。例如，项目评估方法使政策制定者能够改进社会项目，或使公共卫生官员能够更好地针对传染病的缓解。该项目将为三种现代程序评估方法开发新的识别、估计和推理结果：二叉树散布方法、综合控制方法和回归不连续方法。这项研究的结果将为研究人员提供新的方法论工具来改进实证工作。研究生将接受新方法的指导和培训，并获得第一手研究经验。将开发通用软件来实施新的方法。这项研究项目将提供数理统计和计量经济学的新发展。对于与机器学习技术密切相关的双散射方法，将开发新的统一推理方法，这些方法建立在概率论强大的近似工具的基础上。这些结果将为可能具有异质性处理效应的因果推断模型提供关于形状限制的有效置信带和假设检验。对于综合控制方法，将使用高维统计文献中的非渐近概率集中思想来开发新的预测区间。还将研究基于Bootstrap的这些预测区间的实际实现。对于回归间断设计，该项目将为具有持续期类型结果的设置开发新的识别、估计、推断和证伪方法，并允许协变量调整和多次截断。稳健的偏差校正推断方法将与调整参数选择方法一起开发。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Regression Discontinuity Designs

• DOI: 10.1146/annurev-economics-051520-021409
• 发表时间: 2022-01-01
• 期刊: ANNUAL REVIEW OF ECONOMICS
• 影响因子: 5.6
• 作者: Cattaneo, Matias D.;Titiunik, Rocio
• 通讯作者: Titiunik, Rocio