FAI: causal and semi-parametric inference for explanations of disparities and disparity-correcting modeling

FAI：用于解释视差和视差校正建模的因果和半参数推理

• 批准号: 2040804
• 负责人: Ilya Shpitser
• 金额: $ 39.99万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040804&HistoricalAwards=false
• 关键词: FAI; causal; semi; parametric; inference

As learning algorithms become ubiquitous in our lives, many have expressed concerns about the potentially harmful biases and disparities that may arise when these algorithms use sensitive features in the data --- such as race, age, gender, or health --- inappropriately. This project aims to understand and correct for these disparities using causal inference, which aims to use data to quantify cause--effect relationships. Such relationships can be uncovered by trying to predict the change in an effect when a cause variable takes on a different value than one it usually attains. For example, ethnic disparities in health outcomes may arise from different rates of pre-existing comorbidities in different ethnic groups, or from differences in care arising from implicit biases, or from some other mechanisms unmeasured in the data. The indirect effect on health outcome of group-dependent rates of comorbidities can be conceptualized as follows. Measure the health outcomes in patients from ethnic Group A, then use a reliable model to predict outcomes for the same patients with comorbidity rates artificially set to that of ethnic Group B, while leaving everything else the same, and compare the two. Disparity between the measured and predicted health outcomes point to differing comorbidity rates as the cause. Similarly, a direct effect could be revealed by comparing health outcomes in patients from ethnic Group A with predicted health outcomes in that same group with all variables participating in known indirect mechanisms giving rise to disparities left intact, but the variable indicating ethnicity changed to that for Group B. Such a direct effect may be viewed as the proportion of the overall effect of ethnicity on the outcome not explained by indirect effects. This project aims to develop methods that use data to predict how such hypothetical comparisons would turn out, use the results to better understand mechanisms of disparities in healthcare, and build predictive models that are aware, and can correct for undesirable mechanisms of disparity.In this project, the investigator aims to address conceptual, methodological, and practical gaps in explaining disparities via their causal mechanisms and building models and tools that can correct for mechanisms deemed impermissible. An example of such a mechanism is a direct dependence of a decision or outcome on perceived race or gender. The investigator will develop methods that can assess the extent to which disparities in outcomes with respect to a sensitive feature can be attributed to distinct causal pathways. To address challenges causal inference faces in complex high-dimensional settings, the investigator will adopt modern semi-parametric methods that are able to use machine learning models while retaining desirable properties of robustness and rapids rates of convergence. In addition, the investigator will develop a novel combination of methods from causal and semi-parametric inference, and constrained optimization to create predictive models and decision support tools that use data efficiently while preventing impermissible mechanisms of disparity from operating, for instance by ensuring that perceived ethnicity has no direct effect on outcomes or decisions made. The approach will be applied to quantifying disparities and building decision support tools using a complex data set obtained from electronic health records at Johns Hopkins University. All methods will be implemented in an open-source software package Ananke.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.

随着学习算法在我们的生活中变得无处不在，许多人对这些算法不适当地使用数据中的敏感特征（如种族、年龄、性别或健康状况）时可能出现的潜在有害偏见和差异表示担忧。本项目旨在使用因果推理来理解和纠正这些差异，其目的是使用数据来量化因果关系。当一个原因变量的值与它通常达到的值不同时，可以通过尝试预测效果的变化来揭示这种关系。例如，健康结果方面的种族差异可能是由于不同种族群体先前存在的合并症的比率不同，或由于隐性偏见引起的护理差异，或由于数据中未测量的一些其他机制。合并症的群体依赖率对健康结果的间接影响可以概念化如下。测量A族患者的健康结果，然后使用一个可靠的模型来预测相同患者的结果，人为地将合并症率设定为B族患者的合并症率，而其他一切保持不变，并将两者进行比较。测量和预测的健康结果之间的差异表明，不同的合并症发生率是原因。同样，将a族裔患者的健康结果与同一族裔患者的预测健康结果进行比较，可以揭示直接影响，其中所有参与已知间接机制的变量导致差异保持不变，但表明族裔的变量变为b族裔的变量。这种直接影响可被视为族裔对结果的总体影响所占比例，而非间接影响所解释。该项目旨在开发方法，利用数据预测这种假设比较的结果，利用结果更好地理解医疗保健中的差异机制，并建立预测模型，以意识到并能够纠正不希望看到的差异机制。在这个项目中，研究者的目标是解决概念上、方法上和实践上的差距，通过因果机制来解释差异，并建立模型和工具来纠正被认为不允许的机制。这种机制的一个例子是一个决定或结果直接依赖于感知到的种族或性别。研究者将开发方法，以评估与敏感特征相关的结果差异可归因于不同因果途径的程度。为了解决复杂高维环境下因果推理面临的挑战，研究者将采用现代半参数方法，该方法能够使用机器学习模型，同时保持理想的鲁棒性和快速收敛率。此外，研究者将开发一种新的方法组合，从因果和半参数推理，以及约束优化，以创建预测模型和决策支持工具，有效地使用数据，同时防止不允许的差异机制运作，例如，通过确保感知的种族对结果或决策没有直接影响。该方法将应用于利用从约翰霍普金斯大学电子健康记录中获得的复杂数据集来量化差异和建立决策支持工具。所有方法都将在开源软件包Ananke中实现。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。

项目成果

Proximal mediation analysis

近端中介分析

• DOI: 10.1093/biomet/asad015
• 发表时间: 2023
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Dukes, Oliver;Shpitser, Ilya;Tchetgen Tchetgen, Eric J
• 通讯作者: Tchetgen Tchetgen, Eric J

Optimal Training of Fair Predictive Models

公平预测模型的优化训练

• 发表时间: 2022
• 期刊: Proceedings of the First Conference on Causal Learning and Reasoning
• 作者: Razieh Nabi, Daniel Malinsky