III: Medium: CARE: Interactive Systems for Scalable, Causal Data Science

III：媒介：CARE：可扩展因果数据科学的交互式系统

• 批准号: 2312561
• 负责人: Yongjoo Park
• 金额: $ 120万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312561&HistoricalAwards=false
• 关键词: III; Medium; CARE; Interactive; Systems

Advances in machine learning, coupled with advances in scalable data processing, have resulted in highly accurate predictions of quantities of interest. Yet, despite the advances in machine learning and data systems, many data practitioners cannot easily answer causal inference questions in observational data settings. This project will build a novel computer system to allow businesses, academics, and the public to perform effective and intuitive causal exploration. The main novelty of this project will be an end-to-end, causal data exploration system that allows users to ask direct, causal questions and visually experience cause-effect relationships, while the system automatically optimizes for real-time interactions. Using the system, an office employee can ask "What would have been the effect on sales last year had we increased advertising expenditure targeted at women?"; an academic can ask "Did improved educational attainment cause a wage increase?"; a member of the public can ask "Did lack of exercise cause my gain in weight?"This project will develop a scalable, CAusal-RElational (CARE) data system for end-to-end causal data exploration. CARE will let users experience causality by allowing explicit, real-time interventions with causal data modeling, do-calculus querying, and intervention-centric visualization. CARE will accelerate this broad range of tasks simultaneously by optimizing the underlying data layout and using emerging hardware (e.g., GPU, TPU) in consideration of user-specific data access and computational patterns. This project will address three fundamental research challenges: (1) data modeling - designing a causality-driven data model for effortless causal modeling and systems optimization, (2) efficient query processing - rapidly estimating accurate causal treatment effects for large datasets, (3) declarative querying and interactive visualization - assisting users in easily expressing their causal queries and intuitively understanding causality. These research thrusts will be evaluated by measuring system performance and asking human evaluators about their experiences. This research effort will enable interactive, low-effort causal inference, making this crucial analysis tool accessible to all.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.

机器学习的进步，再加上可扩展数据处理的进步，导致了高度准确的兴趣量的预测。然而，尽管机器学习和数据系统取得了进步，但许多数据从业人员无法轻易回答观察数据设置中的因果推理问题。该项目将建立一个新颖的计算机系统，以允许企业，学者和公众进行有效，直观的因果探索。该项目的主要新颖性将是一个端到端的因果数据探索系统，该系统允许用户提出直接的，因果问题并在视觉上经历原因效应关系，而该系统自动为实时互动进行了优化。使用该系统，办公室员工可以问：“如果我们增加针对女性的广告支出，去年将对销售的影响？”；学者可以问“改善的教育程度是否导致工资增加？”；公众可能会问“缺乏运动会导致我体重增加吗？”这个项目将开发可扩展的，因果关系（护理）数据系统，以进行端到端的因果数据探索。护理将使用户通过允许通过因果数据建模，DO-Calculus查询和以干预为中心的可视化进行明确的实时干预来体验因果关系。考虑到特定于用户的数据访问和计算模式，CARE将通过优化基础数据布局并使用新兴硬件（例如GPU，TPU）同时加速这一广泛的任务。 This project will address three fundamental research challenges: (1) data modeling - designing a causality-driven data model for effortless causal modeling and systems optimization, (2) efficient query processing - rapidly estimating accurate causal treatment effects for large datasets, (3) declarative querying and interactive visualization - assisting users in easily expressing their causal queries and intuitively understanding causality.这些研究推力将通过测量系统性能并询问人类评估者有关其经验来评估。这项研究工作将使互动性，低效果的因果推论，使该奖项可供所有人访问。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。