Master of Science in Data Science
Computer Science, Mathematics
College of Science & Mathematics
Umashanger Thayasivam, Ph.D.
Committee Member 1
Gregory Ditzler, Ph.D.
Committee Member 2
Shen Shyang Ho, Ph.D.
decision science, automated decision-making, interpretable decision-making
Computer Sciences | Data Science | Mathematics
This abstract explores two key areas in decision science: automated and interpretable decision making. In the first part, we address challenges related to sparse user interaction data and high item turnover rates in recommender systems. We introduce a novel algorithm called Multi-View Interactive Collaborative Filtering (MV-ICTR) that integrates user-item ratings and contextual information, improving performance, particularly for cold-start scenarios. In the second part, we focus on Student Prescription Trees (SPTs), which are interpretable decision trees. These trees use a black box "teacher" model to predict counterfactuals based on observed covariates. We experiment with a Bayesian hierarchical binomial regression model as the teacher and employ statistical significance testing to control tree growth, ensuring interpretable decision trees. Overall, our research advances the field of decision science by addressing challenges in automated and interpretable decision making, offering solutions for improved performance and interpretability.
Lentini, Maria, "MACHINE LEARNING AND CAUSALITY FOR INTERPRETABLE AND AUTOMATED DECISION MAKING" (2023). Theses and Dissertations. 3157.