CRII: SCH: Towards robustness to data disparities: a framework for efficient and reliable data-driven decision-making tools for all

该奖项的全部或部分资金来自2021年美国救援计划法案(公法117-2)。机器学习(ML)最有前途的应用之一是它能够指导个性化决策，特别是在医疗保健的背景下。预测性ML模型可以帮助临床医生识别不良结果的高风险患者，使他们能够做出关于预防措施的明智决定。因果ML模型可以帮助临床医生和患者了解干预的效果，使他们能够就治疗方案做出更明智的决定。重要的是，预测性和因果最大似然方法的可靠性取决于用于开发它们的数据的质量。不幸的是，数据质量往往反映了医疗保健获取和质量方面的系统性不平等，导致了数据差异。医疗质量不平等的例子包括黑人患者不太可能得到专家的转介，或者女性的疼痛不太可能受到重视，这两者都可能导致诊断和治疗的延误。这意味着从人口的特定亚群收集的数据更容易遗漏。在准入方面，数据显示，黑人和西班牙裔群体更有可能没有保险，更不可能有通常的医疗保健场所。这导致在观察性数据中人口亚群的代表性不足，例如通常用于开发ML模型的电子健康记录。在这项建议中，我们将开发和理论分析稳健的最大似然方法(包括预测和因果)，以改善数据差异的影响。这项拟议的研究有两个主要方面。第一个重点是开发用于诊断的预测工具，这些工具对由于少数群体代表性不足而导致的不准确具有很强的抵抗力。我们将开发模型训练方法，阻止模型学习反映数据偏差而不是真正的因果机制的模式。我们将从理论上分析我们的模型的稳健性和效率。第二个重点是开发对数据缺失和测量误差具有健壮性的干预措施的因果影响的估计方法。虽然大多数现有的工作试图估计干预的因果影响，但这个项目将研究因果估计的区间或界限的估计，这反映了收集的数据质量的不确定性。我们从理论上分析了我们在使用有限数据进行培训时的可信度和紧密性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).One of the most promising applications of machine learning (ML), is its ability to guide personalized decision making, especially in the context of healthcare. Predictive ML models can help clinicians identify patients at high risk of adverse outcomes, enabling them to make informed decisions about preventative measures. Causal ML models can help clinicians and patients understand the effects of interventions enabling them to make more informed decisions about the treatment options. Importantly, the reliability of predictive and causal ML methods depends on the quality of data used to develop them. Unfortunately, data quality often reflects systemic inequalities in both access to and quality of healthcare leading to data disparities. Examples of unequal quality of care include settings in which Black patients are less likely to receive referrals to specialists or in which women’s pain is less likely to be taken seriously, both leading to potential delays in diagnosis and treatment. This means that data collected from specific subgroups of the population are more prone to missingness. In terms of access, data reveal that Black and Hispanic groups are more likely to be uninsured and less likely to have a usual place to go to for medical care. This results in the underrepresentation of subgroups of the population in observational data such as electronic health records typically used to develop ML models. In this proposal, we will develop and theoretically analyze robust ML methods (both predictive and causal) that ameliorate the effects of data disparities. The proposed research has two main prongs. The first prong focuses on developing prediction tools for diagnosis that are robust to inaccuracies due to underrepresentation of minorities. We will develop model training methods that discourage the models from learning patterns that are reflective of data biases rather than true causal mechanisms. We will theoretically analyze the robustness and efficiency of our models. The second prong focuses on developing methods for estimation of causal effects of interventions that are robust to data missingness and measurement error. While most existing work attempts to estimate the causal effect of an intervention, this project will study the estimation of intervals or bounds on the causal estimates which reflect the uncertainty in the quality of the collected data. We theoretically analyze the credibility and tightness of our bounds when trained using limited data.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.