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
基本信息
- 批准号:2241575
- 负责人:
- 金额:$ 45.32万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
随着大数据驱动技术继续用于高风险决策,对快速,易于解释的大数据分析算法的需求变得更加重要。大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并递归地用于分析。 然而,这些划分方法背后的基本原理并没有得到很好的理解,因为机器学习方法在应用程序中的广泛采用并不总是得到理论和方法工具的开发的支持,以了解它们的属性。 该研究项目将研究机器学习和其他大数据分析方法中使用的算法背后的基本原理。 然后,该研究将开发新的和改进的机器学习和其他大数据分析方法,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数据分析,而且还将普遍改善决策,提高经济增长,并帮助美国成为大数据分析和机器学习的全球领导者。正式研究自适应分区和其他灵活学习方法的技术挑战是,递归划分方案难以解释。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,提供积极和消极的结果。 该研究将开发新的治疗效果估计和推理方法,并指导项目评估和因果推理的实践。 一个主要的结果表明,许多流行的递归划分方法的异质性处理效果估计可以逐点(一致)不一致的支持条件变量。其他结果表明,自适应斜决策树可以具有与神经网络相当的准确性。 该研究还研究了基于非线性划分的方法,并将其应用于分位数回归和治疗效果。作为这项研究的一部分,还将开展因果推理和方案评估的附带项目。 这项研究的结果将改善大规模数据的分析,如用于决策的数据,从而改善项目评估。 除了改善经济决策和经济增长外,该奖项还将确立美国在项目评估方面的全球领导地位。该奖项反映了NSF的法定使命,并被认为值得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来支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Matias Cattaneo其他文献
A Permutation Test and Estimation Alternatives for the Regression Kink Design
回归扭结设计的排列测试和估计替代方案
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Alberto Abadie;David Card;Matias Cattaneo;Raj Chetty;Avi Feller;Edward Glaeser;Paul Goldsmith;Guido Imbens;Maximilian Kasy;Larry Katz;Zhuan Pei;Mikkel Plagborg;Guillaume Pouliot - 通讯作者:
Guillaume Pouliot
Matias Cattaneo的其他文献
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{{ truncateString('Matias Cattaneo', 18)}}的其他基金
Conference: Statistical Foundations of Data Science and their Applications
会议:数据科学的统计基础及其应用
- 批准号:
2304646 - 财政年份:2023
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
Nonparametric Estimation and Inference with Network Data
网络数据的非参数估计和推理
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2210561 - 财政年份:2022
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
New Developments in Methodology for Program Evaluation
项目评估方法的新进展
- 批准号:
2019432 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
Collaborative Research: Robust Inference for Kernel Smoothing and Related Problems
协作研究:核平滑及相关问题的鲁棒推理
- 批准号:
1947805 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
A Random Attention Model: Identification, Estimation and Testing
随机注意力模型:识别、估计和测试
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1628883 - 财政年份:2016
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
Collaborative Research: Flexible and Robust Data-driven Inference in Nonparametric and Semiparametric Econometrics
协作研究:非参数和半参数计量经济学中灵活且稳健的数据驱动推理
- 批准号:
1459931 - 财政年份:2015
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
New Methodological Developments for Inference in the Regression-Discontinuity Design
回归-不连续性设计中推理的新方法论发展
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1357561 - 财政年份:2014
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
Collaborative Research: Non-Standard Asymptotic Theory for Semiparametric Estimators
合作研究:半参数估计的非标准渐近理论
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1122994 - 财政年份:2011
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
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合作研究:基于核的半参数估计器的小带宽渐近理论
- 批准号:
0921505 - 财政年份:2009
- 资助金额:
$ 45.32万 - 项目类别:
Standard Grant
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