Partitioning-Based Learning Methods for Treatment Effect Estimation and Inference

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用方法是使用通常称为自适应分区的方法，在这种方法中，对大数据集进行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发展的支持，以理解它们的属性。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著改善机器学习和其他大数据分析，而且还将改善总体决策，促进经济增长，并帮助美国成为大数据分析和机器学习的全球领导者。正式研究自适应划分和其他灵活学习方法的一个技术挑战是，通常递归划分方案引入的随机性难以解释。本研究将为适应性分区学习和其他灵活学习方法提供一系列理论和方法上的结果，提供积极和消极的结果。本研究将发展新的治疗效果估计和推断方法，指导治疗方案评价和因果推断的实践。其中一个主要结果表明，在条件变量的支持下，许多常用的异构处理效果估计递归划分方法可能是点（一致）不一致的。其他结果表明，自适应倾斜决策树具有与神经网络相当的精度。研究了非线性划分方法在分位数回归和治疗效果方面的应用。关于因果推理和项目评估的衍生项目也将作为本研究的一部分进行。本研究的结果将改进对大规模数据的分析，例如那些用于决策的数据，从而改进项目评估。除了改善经济决策和经济增长，研究结果还将确立美国在项目评估方面的全球领先地位。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。

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.