权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

III: SMALL: Moving Beyond Knowledge to Action: Evaluating and Improving the Utility of Causal Inference

III：小：超越知识到行动：评估和提高因果推理的实用性

基本信息

批准号：
1907951
负责人：
Samantha Kleinberg
金额：
$ 49.95万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907951&HistoricalAwards=false
关键词：
III SMALL Moving Beyond Knowledge

项目摘要

One of the key recent advances in machine learning is the ability to learn causal structures from observational data. Unlike correlations, causes let us robustly predict the future and identify which variables to intervene on to potentially change it. As a result, many computational methods have been introduced to better discover causes from the large datasets that are increasingly becoming available. However, algorithms for finding causes are mainly evaluated on how accurately they can recover ground truth. This assumes that the most complete and accurate causal model will be the most useful one, but this assumption has not been tested and people often struggle to make sense of complex information. Causal models can also be used to better understand the effects of actions, which could further improve decisions. While current methods identify the effects of turning a variable on or off, this is not the right level of detail for an individual making choices such as a person with diabetes deciding what specific food to consume for breakfast. Further, the users of the output of causal inference are not those developing the methods, but rather people with varying levels of background knowledge and perceived expertise. This project focuses on reducing the gap between machine learning and human decision-making by quantifying the utility of causal models, introducing new methods that make causal models more useful and usable, and leveraging the results to improve everyday decisions around diet and exercise. This project aims to close the loop from data to knowledge to action, through better metrics for evaluating causal inference, and algorithms that make causal models more useful and personalized. This work will advance our ability to effectively use the output of machine learning, and encourage the development of methods that produce output with high utility. First, this project develops novel ways to automatically evaluate the utility of a set of inferred causes, which allow algorithms to be compared along this new dimension that provides more insight into real-world use. In particular, new metrics are developed that take into account model, user, and context features to allow causal models to be automatically scored on how useful they are for decision-making. Second, the developed metrics are used to guide development of more useful models that accurately predict the effects of interventions and incorporate mechanistic information. A key gap translating causal models to real-world use is the need to predict the result of interventions that may not directly map to variables (e.g. drinking orange juice is not the same as directly increasing glucose). The new methods developed can predict intervention effects using simulation, and map models to mechanistic information to enable further insight. Lastly, the project demonstrates that these enhanced causal models can improve real-life decisions. The project can help make the output of machine learning actionable, and may have applications in many important decision-making scenarios related to health, finance, and personal transportation. The research may more generally improve decision-making, and can be applied to areas as diverse as reducing distracted driving and understanding the impact of choices on energy usage.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.

机器学习最近的一个关键进展是能够从观测数据中学习因果结构。与相关性不同，原因让我们能够稳健地预测未来，并确定哪些变量可以干预以潜在地改变它。因此，许多计算方法已经被引入，以更好地从越来越多的大型数据集中发现原因。然而，用于查找原因的算法主要根据它们恢复地面真相的准确性进行评估。这假设最完整和准确的因果模型将是最有用的模型，但这一假设尚未得到验证，人们往往难以理解复杂的信息。因果模型也可以用来更好地理解行为的影响，这可以进一步改善决策。虽然目前的方法可以识别打开或关闭变量的影响，但这并不是个人做出选择的正确细节水平，例如糖尿病患者决定早餐吃什么特定食物。此外，因果推理输出的用户不是开发方法的人，而是具有不同水平的背景知识和感知专业知识的人。该项目的重点是通过量化因果模型的效用来减少机器学习和人类决策之间的差距，引入使因果模型更有用和可用的新方法，并利用结果来改善日常饮食和运动决策。该项目旨在通过更好的评估因果推理的指标，以及使因果模型更有用和个性化的算法，闭合从数据到知识再到行动的循环。这项工作将提高我们有效利用机器学习输出的能力，并鼓励开发产生高实用性输出的方法。首先，该项目开发了新的方法来自动评估一组推断原因的效用，这使得算法能够沿着沿着这个新的维度进行比较，从而提供对现实世界使用的更多见解。特别是，开发了考虑模型、用户和上下文特征的新指标，以允许因果模型根据其对决策的有用程度自动评分。其次，开发的指标用于指导更有用的模型，准确地预测干预措施的影响，并纳入机械信息的发展。将因果模型转化为现实世界使用的一个关键差距是需要预测可能不直接映射到变量的干预措施的结果（例如，喝橙子汁与直接增加葡萄糖不同）。开发的新方法可以使用模拟来预测干预效果，并将模型映射到机械信息，以实现进一步的洞察。最后，该项目表明，这些增强的因果模型可以改善现实生活中的决策。该项目可以帮助使机器学习的输出具有可操作性，并可能在与健康，金融和个人交通相关的许多重要决策场景中应用。该研究可能会更普遍地改善决策，并可应用于减少分心驾驶和了解选择对能源使用的影响等不同领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。