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.

大规模和复杂数据的分析在社会中起着越来越重要的作用，机器学习中的创新正在产生越来越强大的预测技术。但是，当我们使用此类数据指导决策时，重要的是要认识到，这些域中的大多数数据集是观察性的，而不是随机的，并且需要仔细的分析以得出有关部署潜在政策的因果关系的正确结论。该研究旨在开发用于数据驱动决策的新方法，这些方法可以利用机器学习的能力和表现力，同时严格地基于非随机数据的因果推理的最佳实践。该项目集中在以下三个统计任务围绕以下三个统计任务：（1）研究在稳定的框架中使用柔性框架的异构治疗效应估计的问题，并逐步开发一个灵活的框架，并逐步开发一个均可构成的依据。所提出方法的准确性取决于我们可以干预的因果信号的复杂性，而不仅仅是其他仅关联信号。（2）考虑最大化结构化政策学习的福利，并研究一种方法，其遗憾是在非参数环境中样本量的反平方根的反向平方根。（3）考虑从依次随机数据中学习最佳停止规则的问题，并在这种情况下提出了一种新的可靠但计算上可行的策略学习方法。所有这些结果基础的统一主题是，它们突出了如何使用半参数统计数据中的经典思想来严格利用决策问题中的精确机器学习预测指标。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来进行评估的支持，这是值得的。