CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Learning causality from data is a vital stepping stone toward building human-level intelligent systems that can make appropriate decisions. In seeking to make an optimal decision for each individual (i.e., personalized decision making), we need to understand the causal relationship between a decision and its consequent outcome. Causal inference provides a principled way to achieve personalized decision making by learning individual-level causal effects from observational data. Its impacts are seen in a broad spectrum of application domains. However, existing causal inference frameworks are mostly data-driven and face multifaceted challenges (at the assumption-, data-, and application-level) when applied in real-world observational studies. Despite that, a vast amount of prior human knowledge manifests itself in different ways and could be leveraged to tackle these challenges. Although abundant human knowledge provides great opportunities, its complex nature coupled with observational data also imposes tremendous hurdles. This project aims to bridge the gap between what can be accessed (i.e., a large amount of observational data across different domains and human knowledge in different formats) and what is desired (i.e., more effective causal inference to advance personalized decision making). This project develops a suite of novel causal inference models and algorithms to analyze observational data by harnessing the power of human knowledge and gaining deeper insights to advance personalized decision making. First, it leverages relational knowledge that describes the relations among data instances in observational data, investigates its role in relaxing overly optimistic assumptions for causal inference. Second, it explores meta knowledge that depicts distinct properties of observational data and develops principled causal inference models and algorithms to incorporate such knowledge. Third, it aims to improve the utility of existing data-driven causal inference frameworks by harnessing application knowledge, which characterizes the unique needs of real-world applications. The outcomes of this project will enable researchers and practitioners to assimilate massive amounts of observational data, across numerous application domains, and leverage abundant human knowledge, to benefit scientific discovery and informed decision making. Outcomes of this project will be integrated into the existing curricula and new courses. This project will also provide research opportunities to undergraduate and graduate students, especially female and underrepresented minorities. Customized research and teaching components will be designed and implemented to attract K-12 students in STEM education and engage them in causal inference and data science research. Last but not least, this project will improve student success and retention via a unique educational decision making component. This approach will optimize current education systems, for the benefit of generations of students to come. 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.