Partitioning-Based Learning Methods for Treatment Effect Estimation and Inference

随着大数据驱动的技术继续用于高风险的决策，对用于大数据分析的快速、易于解释的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，在这种方法中，大数据集被分区并在分析中递归使用。然而，这些划分方法背后的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，以了解它们的性质。这个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，研究将为机器学习和其他大型数据分析开发新的和改进的方法，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，并有助于确立美国在大数据分析和机器学习方面的全球领先地位。形式上研究自适应分区和其他灵活学习方法的一个技术挑战是，经常递归的分区方案引入的随机性很难解释。这项研究将为基于自适应分区的学习方法和其他灵活的学习方法提供一系列的理论和方法结果，既有积极的结果，也有消极的结果。这项研究将开发新的治疗效果评估和推断方法，并指导实践中的方案评价和因果推断。其中一个主要结果表明，在条件变量的支持下，许多流行的用于异质治疗效果估计的递归划分方法可能是逐点(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。研究还研究了基于非线性划分的方法及其在分位数回归和处理效果中的应用。作为这项研究的一部分，还将开展关于因果推理和方案评估的衍生项目。这项研究的结果将改进对大规模数据的分析，例如用于决策的数据，从而改善项目评估。除了改善经济决策和经济增长，这一结果还将使美国成为项目评估的全球领导者。这一奖项反映了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。

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.