CAREER: From Fragile to Fortified: Harnessing Causal Reasoning for Trustworthy Machine Learning with Unreliable Data

Despite their popularity and success, machine learning (ML) methods still have major limitations. This project addresses limitations that arise because of unrealistic requirements concerning the data used for model development. Specifically, ML methods require massive amounts of data that is error-free and accurate, which is not always available. In this project, ML methods that work with imperfect data by leveraging causal reasoning are developed. The technical contributions made in this project will enable combining clinician-acquired causal knowledge with imperfect data to tackle chronic pain, a critical health issue impacting millions and costing billions annually. Beyond its impact on managing chronic pain, this project will enable undergraduate and graduate student education through projects related to the research objectives. The project has three thrusts. Thrust 1 will leverage known causal mechanisms to create robust and efficient predictive models. Unlike current approaches that penalize model capacity by constraining its smoothness, this project will focus on schemes that penalize models encoding concepts that contradict known causal mechanisms. Thrust 2 will develop methods to quantify uncertainty in predictions by building upon causal sensitivity analysis, and developing methods to quantify uncertainty in nonparametric independence tests. Thrust 3 will leverage known causal mechanisms to create robust and efficient offline reinforcement learning (RL) models. It will also enable credible estimation of uncertainty to guide policy optimization. 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.