CAREER: Robust Policy Learning for Safe and Reliable Algorithmic Decision Making from Observational Data in Sensitive Applications

机器学习的一些最具影响力的应用不仅仅是预测，而是在正确的时间针对正确的目标采取正确的行动。与预测不同，行动有后果，因此，在寻求采取正确行动时，必须设法了解其因果关系。本项目研究敏感应用中从观测数据中提取因果最大化个性化决策规则的问题。在医学和公民学等领域已经变得丰富的观测数据，缺乏实验操作，因此孤立的因果效应被复杂的选择过程所掩盖，这种现象被称为混淆，而且这些数据在其他方面也是混乱的，嘈杂的，有偏见的，经常缺失的。尽管有丰富和丰富的观测数据的希望，目前的方法不能处理它所带来的独特挑战，并可能导致不可靠、不安全和不公平的决策，不适合敏感的应用。该项目的目标是建立一个由严谨的理论和可靠的方法组成的综合框架，以弥补这一差距，并使根据观测数据训练的可靠决策系统成为可能。研究和教育计划通过学生咨询和课程开发相结合，并包括有针对性的外展努力，以扩大代表性不足群体的参与。研究将沿着三个主要方向进行。首先是发展在未观察到的混杂因素存在下的算法决策的方法和理论。虽然现有的方法依赖于各种无法验证的假设，以确保因果关系的点识别，但该项目将开发强大的学习方法，以产生具有安全性和/或改进证书的政策，这些证书由理论保证支持，而不依赖于精确的识别。其次是为政策学习开发鲁棒和最优加权的方法和理论，以解决稳定性、有限重叠、事件时间数据以及噪声和缺失观测等问题。第三是研究从观测数据中训练的决策政策的算法公平性。该项目将开发在差异指标无法从数据中点识别的情况下的公平性特征，无论是由于有偏见的选择还是由于缺少属性和标签，以及可以在此类情况下审计和执行公平性的方法。通过这些重点，该项目将推进机器学习、因果推理和优化交叉领域的知识，并扩大它们与新问题领域集成的范围。该奖项反映了美国国家科学基金会的法定使命，并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。

Some of the most impactful applications of machine learning are not just about prediction but are rather about taking the right action directed at the right target at the right time. Actions, unlike predictions, have consequences and so, in seeking to take the right action, one must seek to understand its causal effect. This project deals with the problem of extracting causal-effect-maximizing personalized decision rules from observational data in sensitive applications. Observational data, which have become plentiful in domains such as medicine and civics, lack experimental manipulation so that isolated causal effects are obscured by complex selection processes, a phenomenon known as confounding, and such data are also otherwise messy, noisy, biased, and often missing. Despite the promise of rich and plentiful observational data, current approaches cannot handle the unique challenges it poses and can lead to unreliable, unsafe, and unfair decision making unfit for sensitive applications. The goal of this project is to create a comprehensive framework of rigorous theory and robust methodology to address this gap and enable trustworthy decision-making systems trained on observational data. The research and education plans are integrated through student advising and curriculum development and include targeted outreach efforts to broaden participation of underrepresented groups.The research will proceed along three primary directions. The first is to develop methods and theory for algorithmic decision making in the presence of unobserved confounders. Whereas existing approaches tenuously rely on various unverifiable assumptions that ensure point-identification of causal effects, the project will develop robust learning methods that produce policies with certificates of safety and/or improvement backed by theoretical guarantees that do not rely on exact identification. The second is to develop methods and theory for robust and optimal weighting for policy learning to address issues of stability, limited overlap, time-to-event data, and noisy and missing observations. The third is to investigate algorithmic fairness of decision policies trained from observational data. The project will develop characterizations of fairness in settings where disparity metrics cannot be point-identified from data, whether due to biased selection or missing attributes and labels, as well as methods that can audit and enforce fairness in such settings. Through these thrusts, the project will both advance knowledge at the intersection of machine learning, causal inference, and optimization as well as broaden the scope of their integration with new problem domains.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.