Efficient Estimation of Treatment Effects via Nonparametric Machine Learning

技术的进步在观察性研究中创造了大量的大规模数据集，为评估各种治疗方法的有效性带来了前所未有的机会。大规模观测数据的海量性和混杂因素的高维性给现有的因果分析方法带来了巨大的挑战。相应的统计含义是，即使是少量的偏差也很容易导致错误的结论。由于可扩展计算技术的快速发展，非参数机器学习方法具有通过采用数据驱动的降维策略来降低偏差的强大潜力。然而，必须仔细考虑，以实现潜在的研究潜在的因果机制。该项目旨在开发具有理论见解的尖端统计方法，用于使用深度神经网络进行因果关系分析。新的统计工具满足了各科学领域从大规模观测数据中探索因果关系的迫切需要。该研究对于现代复杂数据的因果关系分析具有重要的应用价值。该项目将通过课程开发、开源软件开发以及本科生和研究生培训来整合研究和教育。 PI将开发一种新的统一方法，并通过彻底的理论论证，使用深度神经网络有效估计因果效应。然后，该方法将被应用于具有二进制，多值或连续值治疗变量的大规模数据集。将探讨三个相互关联的专题。具体而言，PI将通过广义优化估计提供关于学习治疗效果的新视角。因此，两个方便和有效的估计治疗效果将分别在第二和第三个主题。估计量涉及一个将由深度神经网络近似的滋扰模型，这将在第一个主题中进行研究。一般的优化框架包括平均值，分位数和非对称最小二乘治疗效果作为特殊情况。该方法充分利用了大规模数据样本量大的特点，有效地防止了模型的误设定偏差。该项目涉及为因果研究设计新的机器学习方法和算法、建立理论有效性以及开发有效的推理程序。该奖项反映了NSF的法定使命，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。

Advances in technology have created numerous large-scale datasets in observational studies, which bring unprecedented opportunities for evaluating the effectiveness of various treatments. The complex nature of large-scale observational data, such as its massive volume and high dimensions in confounders, pose great challenges to the existing conventional methods for causal analysis. The corresponding statistical implication is that even a small amount of bias can easily lead to erroneous conclusions. Thanks to the rapid development of scalable computing techniques, nonparametric machine learning methods have a strong potential for bias reduction via employing data-driven strategies for dimensionality reduction. However, careful consideration must be given in order to realize the potential for studying the underlying causal mechanisms. This project aims to develop cutting-edge statistical methods with theoretical insights for causality analysis using deep neural networks. The new statistical tools meet the immediate needs from various scientific areas for exploring causal relationships from large-scale observational data. The research will facilitate the causality analysis of modern complex data with important applications. This project will integrate research and education through course development, open-source software development, and undergraduate and graduate student training. The PI will develop a new unified approach with thorough theoretical justifications for efficient estimation of causal effects using deep neural networks. The method will then be applied to large-scale datasets with binary, multi-valued, or continuous-valued treatment variables. Three interconnected topics will be pursued. Specifically, the PI will offer a new perspective on learning treatment effects through a generalized optimization estimation. As a result, two convenient and efficient estimators of treatment effects will be developed, respectively, in the second and third topics. The estimators involve one nuisance model that will be approximated by deep neural networks, which will be investigated in the first topic. The general optimization framework includes the average, quantile and asymmetric least squares treatment effects as special cases. The methods take full advantage of the large sample size of large-scale data and provide effective protection against model mis-specification bias. The project involves devising new machine learning methods and algorithms for causal studies, establishing the theoretical validity, and developing valid inference procedures. It will promote machine learning methods for causality analysis, and will break new ground in drawing causal inference from large-scale observational datasets.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.