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

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.