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

机器学习的进步，加上可扩展数据处理的进步，已经导致了对感兴趣数量的高度准确的预测。然而，尽管机器学习和数据系统取得了进步，但许多数据从业者无法轻松回答观测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，使企业、学者和公众能够进行有效和直观的因果探索。该项目的主要新颖之处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直观地体验因果关系，同时系统会自动优化实时交互。使用该系统，办公室员工可以问：“如果去年我们增加了针对女性的广告支出，对销售额会有什么影响？”学者可以问：“教育程度的提高导致了工资的增长吗？”公众可以问：“是缺乏锻炼导致我体重增加的吗？”该项目将开发一个可扩展的因果关系（CARE）数据系统，用于端到端的因果数据探索。CARE将通过因果数据建模、计算查询和以干预为中心的可视化，允许明确的实时干预，让用户体验因果关系。考虑到用户特定的数据访问和计算模式，CARE将通过优化底层数据布局和使用新兴硬件（例如GPU、TPU）来同时加速这一广泛的任务。该项目将解决三个基础研究挑战：(1)数据建模-设计因果关系驱动的数据模型，以实现轻松的因果建模和系统优化；(2)高效查询处理-快速估计大型数据集的准确因果处理效果；(3)声明性查询和交互式可视化-帮助用户轻松表达因果查询并直观地理解因果关系。这些研究重点将通过测量系统性能和询问人类评估人员的经验来评估。这项研究工作将使互动、低成本的因果推理成为可能，使所有人都能使用这一重要的分析工具。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。

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.