Partitioning-Based Learning Methods for Treatment Effect Estimation and Inference

基于分区的治疗效果估计和推理学习方法

• 批准号: 2241575
• 负责人: Matias Cattaneo
• 金额: $ 45.32万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241575&HistoricalAwards=false
• 关键词: Partitioning; Based; Learning; Methods; Treatment

As large data-driven technologies continue to be used in high-stakes decision-making, the need for fast, easily interpretable algorithms for large data analyses has become more important. A common approach in large data analyses is to use what is commonly called adaptive partitioning in which the large data set is partitioned and recursively used in the analyses. However, the rationale behind these partitioning methods is not well understood as the broad adoption of machine learning methods in applications has not always been supported by development of theoretical and methodological tools to understand their properties. This research project will study the rationale behind algorithms used in machine learning and other methods for large data analyses. The research will then develop new and improved methods for machine learning and other large data analyses and apply these methods to several economic problems. The results of this research will not only significantly improve machine learning and other large data analyses, but it will also improve decision-making generally, increase economic growth, and help establish the US as a global leader in large data analyses and machine learning.A technical challenge in formally studying adaptive partitioning and other flexible learning methods is that the randomness introduced by the often-recursive partition scheme is difficult to account for. This research will provide an array of theoretical and methodological results for adaptive partition-based and other flexible learning methods, providing both positive and negative results. The research will develop new treatment effect estimation and inference methods, and guide practice in program evaluation and causal inference. One of the main results shows that many popular recursive partitioning methods for heterogeneous treatment effect estimation can be pointwise (uniformly) inconsistent over the support of the conditioning variables. Other results show that adaptive oblique decision trees can have accuracy on par with neural networks. The research also studies non-linear partitioning-based methods with applications to quantile regression and treatment effects. Spin-off projects on causal inference and program evaluation will also be undertaken as part of this research. The results of this research will improve the analyses of large scale data, such as those used to make decisions and thus improve program evaluation. Besides improving economic decision making and economic growth, the results will also establish the US as a global leader in program evaluation.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.

随着大型数据驱动的技术继续用于高风险决策，对大型数据分析的快速，易于解释的算法的需求变得越来越重要。大型数据分析中的一种常见方法是使用通常称为自适应分区的方法，其中大数据集被分区和递归使用。 但是，这些分区方法背后的理由并没有充分理解，因为在应用程序中的机器学习方法的广泛采用并不总是受到理论和方法论工具来理解其特性的支持。 该研究项目将研究机器学习中使用的算法和其他用于大型数据分析的方法的基本原理。 然后，该研究将开发用于机器学习和其他大型数据分析的新方法，并将这些方法应用于几个经济问题。 这项研究的结果不仅将显着改善机器学习和其他大型数据分析，而且还将一般改善决策，提高经济增长，并帮助将美国作为大型数据分析和机器学习的全球领导者。正式研究适应性分配和其他灵活学习方法的技术挑战是，经常由comportive compution compution cartitution cartition cartition cartitution cartition cartition cartition cartition cartition cartition cartition cartition cartition cartition cartition。这项研究将为基于自适应分区和其他灵活的学习方法提供一系列理论和方法论结果，从而提供积极和负面的结果。 该研究将开发新的治疗效果估计和推理方法，并指导计划评估和因果推断的实践。 主要结果之一表明，许多流行的递归分区方法用于异质治疗效果估计可能在支持条件变量的支持方面（均匀）不一致。其他结果表明，自适应倾斜决策树可以与神经网络相同。 该研究还研究了基于分数回归和治疗效果的非线性分区方法。作为这项研究的一部分，还将进行有关因果推理和计划评估的衍生项目。 这项研究的结果将改善大规模数据的分析，例如用于做出决策的数据，从而改善计划评估。 除了改善经济决策和经济增长外，结果还将确定美国作为计划评估的全球领导者。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。