CAREER: From Fragile to Fortified: Harnessing Causal Reasoning for Trustworthy Machine Learning with Unreliable Data

职业：从脆弱到坚固：利用因果推理，利用不可靠的数据实现值得信赖的机器学习

• 批准号: 2337529
• 负责人: Maggie Makar
• 金额: $ 60万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337529&HistoricalAwards=false
• 关键词: CAREER; Fragile; Fortified; Harnessing; Causal

Despite their popularity and success, machine learning (ML) methods still have major limitations. This project addresses limitations that arise because of unrealistic requirements concerning the data used for model development. Specifically, ML methods require massive amounts of data that is error-free and accurate, which is not always available. In this project, ML methods that work with imperfect data by leveraging causal reasoning are developed. The technical contributions made in this project will enable combining clinician-acquired causal knowledge with imperfect data to tackle chronic pain, a critical health issue impacting millions and costing billions annually. Beyond its impact on managing chronic pain, this project will enable undergraduate and graduate student education through projects related to the research objectives. The project has three thrusts. Thrust 1 will leverage known causal mechanisms to create robust and efficient predictive models. Unlike current approaches that penalize model capacity by constraining its smoothness, this project will focus on schemes that penalize models encoding concepts that contradict known causal mechanisms. Thrust 2 will develop methods to quantify uncertainty in predictions by building upon causal sensitivity analysis, and developing methods to quantify uncertainty in nonparametric independence tests. Thrust 3 will leverage known causal mechanisms to create robust and efficient offline reinforcement learning (RL) models. It will also enable credible estimation of uncertainty to guide policy optimization.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.

尽管它们的知名度和成功，但机器学习（ML）方法仍然存在重大局限性。该项目解决了由于对模型开发的数据的不现实要求而产生的局限性。具体而言，ML方法需要大量无错误和准确的数据，这并不总是可用。在此项目中，开发了通过利用因果推理来与不完美数据一起使用的ML方法。该项目中的技术贡献将使临床医生获得的因果知识与不完美的数据相结合，以应对慢性疼痛，这是一项关键的健康问题，影响了数百万美元，每年耗资数十亿美元。除了对管理慢性疼痛的影响之外，该项目还将通过与研究目标有关的项目实现本科和研究生教育。该项目有三个推力。推力1将利用已知的因果机制来创建强大而有效的预测模型。与当前通过限制模型容量的平稳性来惩罚模型能力的方法不同，该项目将集中在惩罚编码与已知因果机制相矛盾的概念的模型的方案上。推力2将通过建立因果敏感性分析，开发量化非参数独立性测试中不确定性的方法来开发方法来量化预测中的不确定性。推力3将利用已知的因果机制来创建强大而有效的离线增强学习（RL）模型。这也将使不确定性的可信度估计来指导政策优化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。