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Learning Decision Rules with Observational Data

用观察数据学习决策规则

基本信息

批准号：
1916163
负责人：
Stefan Wager
金额：
$ 14万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916163&HistoricalAwards=false
关键词：
Learning Decision Rules Observational Data

项目摘要

The analysis of large-scale and complex data plays an increasingly central role in society, and innovations in machine learning are yielding ever more powerful predictive technologies. However, when we use such data to guide decision making, it is important to recognize that the majority of datasets in these domains are observational rather than randomized in nature, and require careful analysis in order to draw correct conclusions about the causal effect of deploying a potential policy. The research aims to develop new methods for data-driven decision making that can harness the power and expressiveness of machine learning, all while rigorously building on best practices for causal inference from non-randomized data.This project is centered around the following three statistical tasks: (1) Examine the problem of heterogeneous treatment effect estimation in observational studies, and develop a flexible framework that can be used with, e.g., boosting or neural networks. The accuracy of the proposed method depends on the complexity of the causal signal that we can intervene on, not on other merely associational signals. (2) Consider welfare maximizing structured policy learning, and study an approach whose regret decays as the inverse square root of the sample size in a non-parametric setting. (3) Consider the problem of learning optimal stopping rules from sequentially randomized data, and propose a new robust yet computationally feasible approach to policy learning in this setting. A unifying theme underlying all these results is that they highlight how classical ideas from semiparametric statistics can be used to rigorously leverage accurate machine learning predictors in decision-making problems.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.

对大规模复杂数据的分析在社会中发挥着越来越重要的作用，机器学习的创新正在产生越来越强大的预测技术。然而，当我们使用这些数据来指导决策时，重要的是要认识到这些领域的大多数数据集都是观察性的，而不是随机的，需要仔细分析，以便得出关于部署潜在政策的因果影响的正确结论。该研究旨在开发数据驱动决策的新方法，这些方法可以利用机器学习的力量和表现力，同时严格基于从非随机数据进行因果推理的最佳实践。该项目围绕以下三个统计任务：（1）检查观察性研究中异质性治疗效果估计的问题，并制定一个灵活的框架，例如，在一个实施例中，增强或神经网络。所提出的方法的准确性取决于我们可以干预的因果信号的复杂性，而不是其他仅仅是关联信号。(2)考虑福利最大化的结构化政策学习，并研究一种方法，其遗憾衰减为非参数设置中的样本大小的平方根倒数。(3)考虑从顺序随机数据中学习最优停止规则的问题，并提出一种新的鲁棒但计算上可行的策略学习方法。所有这些结果背后的一个统一主题是，它们突出了半参数统计的经典思想如何被用于在决策问题中严格利用准确的机器学习预测器。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智力价值和更广泛的影响审查标准进行评估来支持。